Kom igång med Dokumentinformation

Klientbibliotekets SDK-referens | REST API-referens | Paketexempel|| stöds REST API-version |

Klientbibliotekets SDK-referens-API-referenspaket | | (NuGet) | Exempel | som stöds REST API-version |

Klientbibliotekets SDK-referens | REST API-referens | Paketexempel | | stöds REST API-version |

• En Azure AI-tjänst eller dokumentinformationsresurs. När du har din Azure-prenumeration skapar du en resurs med en enda tjänst eller Azure AI med flera tjänster i Azure Portal för att hämta din nyckel och slutpunkt.

• Du kan använda den kostnadsfria prisnivån (F0) för att prova tjänsten och uppgradera senare till en betald nivå för produktion.

• När resursen har distribuerats väljer du Gå till resurs. Du behöver nyckeln och slutpunkten från resursen som du skapar för att ansluta ditt program till API:et för dokumentinformation. Du klistrar in nyckeln och slutpunkten i koden senare i snabbstarten:

  

• En Azure AI-tjänst eller Formigenkänning resurs. När du har din Azure-prenumeration skapar du en resurs med en enda tjänst eller Azure AI med flera tjänster i Azure Portal för att hämta din nyckel och slutpunkt.

• Du kan använda den kostnadsfria prisnivån (F0) för att prova tjänsten och uppgradera senare till en betald nivå för produktion.

• När resursen har distribuerats väljer du Gå till resurs. Du behöver nyckeln och slutpunkten från resursen som du skapar för att ansluta ditt program till Formigenkänning-API:et. Du klistrar in nyckeln och slutpunkten i koden senare i snabbstarten:

  

4. I dialogrutan Konfigurera det nya projektet anger du doc_intel_quickstart i rutan Projektnamn. Välj sedan Nästa.

4. I dialogrutan Konfigurera det nya projektet anger du form_recognizer_quickstart i rutan Projektnamn. Välj sedan Nästa.

1. Högerklicka på ditt doc_intel_quickstart projekt och välj Hantera NuGet-paket... .

   

2. Välj fliken Bläddra och skriv Azure.AI.FormRecognizer.

3. Markera kryssrutan Include prerelease .

   

4. Välj en version från den nedrullningsbara menyn och installera paketet i projektet.

1. Högerklicka på ditt form_recognizer_quickstart projekt och välj Hantera NuGet-paket... .

   

2. Välj fliken Bläddra och skriv Azure.AI.FormRecognizer. Välj version 4.1.0 i listrutan

   

1. Högerklicka på ditt form_recognizer_quickstart projekt och välj Hantera NuGet-paket... .

   

2. Välj fliken Bläddra och skriv Azure.AI.FormRecognizer. Välj version 4.0.0 i listrutan

   

Om du vill interagera med document intelligence-tjänsten måste du skapa en instans av DocumentIntelligenceClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentIntelligenceClient instans med och dokumentinformationen AzureKeyCredential endpoint.

Om du vill interagera med Formigenkänning-tjänsten måste du skapa en instans av DocumentAnalysisClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentAnalysisClient instans med AzureKeyCredential och din Formigenkänning endpoint.

Lägg till följande kodexempel i filen Program.cs. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

using Azure;
using Azure.AI.DocumentIntelligence;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `DocumentIntelligenceClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentIntelligenceClient client = new DocumentIntelligenceClient(new Uri(endpoint), credential);

//sample document
Uri fileUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf");

AnalyzeDocumentContent content = new AnalyzeDocumentContent()
{
    UrlSource= fileUri
};

Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-layout", content);

AnalyzeResult result = operation.Value;

foreach (DocumentPage page in result.Pages)
{
    Console.WriteLine($"Document Page {page.PageNumber} has {page.Lines.Count} line(s), {page.Words.Count} word(s)," +
        $" and {page.SelectionMarks.Count} selection mark(s).");

    for (int i = 0; i < page.Lines.Count; i++)
    {
        DocumentLine line = page.Lines[i];

        Console.WriteLine($"  Line {i}:");
        Console.WriteLine($"    Content: '{line.Content}'");

        Console.Write("    Bounding polygon, with points ordered clockwise:");
        for (int j = 0; j < line.Polygon.Count; j += 2)
        {
            Console.Write($" ({line.Polygon[j]}, {line.Polygon[j + 1]})");
        }

        Console.WriteLine();
    }

    for (int i = 0; i < page.SelectionMarks.Count; i++)
    {
        DocumentSelectionMark selectionMark = page.SelectionMarks[i];

        Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
        Console.WriteLine($"    State: {selectionMark.State}");

        Console.Write("    Bounding polygon, with points ordered clockwise:");
        for (int j = 0; j < selectionMark.Polygon.Count; j++)
        {
            Console.Write($" ({selectionMark.Polygon[j]}, {selectionMark.Polygon[j + 1]})");
        }

        Console.WriteLine();
    }
}

for (int i = 0; i < result.Paragraphs.Count; i++)
{
    DocumentParagraph paragraph = result.Paragraphs[i];

    Console.WriteLine($"Paragraph {i}:");
    Console.WriteLine($"  Content: {paragraph.Content}");

    if (paragraph.Role != null)
    {
        Console.WriteLine($"  Role: {paragraph.Role}");
    }
}

foreach (DocumentStyle style in result.Styles)
{
    // Check the style and style confidence to see if text is handwritten.
    // Note that value '0.8' is used as an example.

    bool isHandwritten = style.IsHandwritten.HasValue && style.IsHandwritten == true;

    if (isHandwritten && style.Confidence > 0.8)
    {
        Console.WriteLine($"Handwritten content found:");

        foreach (DocumentSpan span in style.Spans)
        {
            var handwrittenContent = result.Content.Substring(span.Offset, span.Length);
            Console.WriteLine($"  {handwrittenContent}");
        }
    }
}

for (int i = 0; i < result.Tables.Count; i++)
{
    DocumentTable table = result.Tables[i];

    Console.WriteLine($"Table {i} has {table.RowCount} rows and {table.ColumnCount} columns.");

    foreach (DocumentTableCell cell in table.Cells)
    {
        Console.WriteLine($"  Cell ({cell.RowIndex}, {cell.ColumnIndex}) is a '{cell.Kind}' with content: {cell.Content}");
    }
}

Kör ditt program

När du har lagt till ett kodexempel i programmet väljer du den gröna Start-knappen bredvid formRecognizer_quickstart för att skapa och köra programmet, eller tryck på F5.

Lägg till följande kodexempel i filen Program.cs. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

using Azure;
using Azure.AI.FormRecognizer.DocumentAnalysis;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `DocumentAnalysisClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentAnalysisClient client = new DocumentAnalysisClient(new Uri(endpoint), credential);

//sample document
Uri fileUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf");

AnalyzeDocumentOperation operation = await client.AnalyzeDocumentFromUriAsync(WaitUntil.Completed, "prebuilt-layout", fileUri);

AnalyzeResult result = operation.Value;

foreach (DocumentPage page in result.Pages)
{
    Console.WriteLine($"Document Page {page.PageNumber} has {page.Lines.Count} line(s), {page.Words.Count} word(s),");
    Console.WriteLine($"and {page.SelectionMarks.Count} selection mark(s).");

    for (int i = 0; i < page.Lines.Count; i++)
    {
        DocumentLine line = page.Lines[i];
        Console.WriteLine($"  Line {i} has content: '{line.Content}'.");

        Console.WriteLine($"    Its bounding box is:");
        Console.WriteLine($"      Upper left => X: {line.BoundingPolygon[0].X}, Y= {line.BoundingPolygon[0].Y}");
        Console.WriteLine($"      Upper right => X: {line.BoundingPolygon[1].X}, Y= {line.BoundingPolygon[1].Y}");
        Console.WriteLine($"      Lower right => X: {line.BoundingPolygon[2].X}, Y= {line.BoundingPolygon[2].Y}");
        Console.WriteLine($"      Lower left => X: {line.BoundingPolygon[3].X}, Y= {line.BoundingPolygon[3].Y}");
    }

    for (int i = 0; i < page.SelectionMarks.Count; i++)
    {
        DocumentSelectionMark selectionMark = page.SelectionMarks[i];

        Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
        Console.WriteLine($"    Its bounding box is:");
        Console.WriteLine($"      Upper left => X: {selectionMark.BoundingPolygon[0].X}, Y= {selectionMark.BoundingPolygon[0].Y}");
        Console.WriteLine($"      Upper right => X: {selectionMark.BoundingPolygon[1].X}, Y= {selectionMark.BoundingPolygon[1].Y}");
        Console.WriteLine($"      Lower right => X: {selectionMark.BoundingPolygon[2].X}, Y= {selectionMark.BoundingPolygon[2].Y}");
        Console.WriteLine($"      Lower left => X: {selectionMark.BoundingPolygon[3].X}, Y= {selectionMark.BoundingPolygon[3].Y}");
    }
}

foreach (DocumentStyle style in result.Styles)
{
    // Check the style and style confidence to see if text is handwritten.
    // Note that value '0.8' is used as an example.

    bool isHandwritten = style.IsHandwritten.HasValue && style.IsHandwritten == true;

    if (isHandwritten && style.Confidence > 0.8)
    {
        Console.WriteLine($"Handwritten content found:");

        foreach (DocumentSpan span in style.Spans)
        {
            Console.WriteLine($"  Content: {result.Content.Substring(span.Index, span.Length)}");
        }
    }
}

Console.WriteLine("The following tables were extracted:");

for (int i = 0; i < result.Tables.Count; i++)
{
    DocumentTable table = result.Tables[i];
    Console.WriteLine($"  Table {i} has {table.RowCount} rows and {table.ColumnCount} columns.");

    foreach (DocumentTableCell cell in table.Cells)
    {
        Console.WriteLine($"    Cell ({cell.RowIndex}, {cell.ColumnIndex}) has kind '{cell.Kind}' and content: '{cell.Content}'.");
    }
}

Kör ditt program

När du har lagt till ett kodexempel i programmet väljer du den gröna Start-knappen bredvid formRecognizer_quickstart för att skapa och köra programmet, eller tryck på F5.

Utdata för layoutmodell

Här är ett kodfragment av förväntade utdata:

  Document Page 1 has 69 line(s), 425 word(s), and 15 selection mark(s).
  Line 0 has content: 'UNITED STATES'.
    Its bounding box is:
      Upper left => X: 3.4915, Y= 0.6828
      Upper right => X: 5.0116, Y= 0.6828
      Lower right => X: 5.0116, Y= 0.8265
      Lower left => X: 3.4915, Y= 0.8265
  Line 1 has content: 'SECURITIES AND EXCHANGE COMMISSION'.
    Its bounding box is:
      Upper left => X: 2.1937, Y= 0.9061
      Upper right => X: 6.297, Y= 0.9061
      Lower right => X: 6.297, Y= 1.0498
      Lower left => X: 2.1937, Y= 1.0498

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa layoutmodellens utdata.

Lägg till följande kodexempel i filen Program.cs. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

using Azure;
using Azure.AI.FormRecognizer.DocumentAnalysis;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `DocumentAnalysisClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentAnalysisClient client = new DocumentAnalysisClient(new Uri(endpoint), credential);

//sample document
Uri fileUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf");

AnalyzeDocumentOperation operation = await client.AnalyzeDocumentFromUriAsync(WaitUntil.Completed, "prebuilt-layout", fileUri);

AnalyzeResult result = operation.Value;

foreach (DocumentPage page in result.Pages)
{
    Console.WriteLine($"Document Page {page.PageNumber} has {page.Lines.Count} line(s), {page.Words.Count} word(s),");
    Console.WriteLine($"and {page.SelectionMarks.Count} selection mark(s).");

    for (int i = 0; i < page.Lines.Count; i++)
    {
        DocumentLine line = page.Lines[i];
        Console.WriteLine($"  Line {i} has content: '{line.Content}'.");

        Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");

        for (int j = 0; j < line.BoundingPolygon.Count; j++)
        {
            Console.WriteLine($"      Point {j} => X: {line.BoundingPolygon[j].X}, Y: {line.BoundingPolygon[j].Y}");
        }
    }

    for (int i = 0; i < page.SelectionMarks.Count; i++)
    {
        DocumentSelectionMark selectionMark = page.SelectionMarks[i];

        Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
        Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");

        for (int j = 0; j < selectionMark.BoundingPolygon.Count; j++)
        {
            Console.WriteLine($"      Point {j} => X: {selectionMark.BoundingPolygon[j].X}, Y: {selectionMark.BoundingPolygon[j].Y}");
        }
    }
}

Console.WriteLine("Paragraphs:");

foreach (DocumentParagraph paragraph in result.Paragraphs)
{
    Console.WriteLine($"  Paragraph content: {paragraph.Content}");

    if (paragraph.Role != null)
    {
        Console.WriteLine($"    Role: {paragraph.Role}");
    }
}

foreach (DocumentStyle style in result.Styles)
{
    // Check the style and style confidence to see if text is handwritten.
    // Note that value '0.8' is used as an example.

    bool isHandwritten = style.IsHandwritten.HasValue && style.IsHandwritten == true;

    if (isHandwritten && style.Confidence > 0.8)
    {
        Console.WriteLine($"Handwritten content found:");

        foreach (DocumentSpan span in style.Spans)
        {
            Console.WriteLine($"  Content: {result.Content.Substring(span.Index, span.Length)}");
        }
    }
}

Console.WriteLine("The following tables were extracted:");

for (int i = 0; i < result.Tables.Count; i++)
{
    DocumentTable table = result.Tables[i];
    Console.WriteLine($"  Table {i} has {table.RowCount} rows and {table.ColumnCount} columns.");

    foreach (DocumentTableCell cell in table.Cells)
    {
        Console.WriteLine($"    Cell ({cell.RowIndex}, {cell.ColumnIndex}) has kind '{cell.Kind}' and content: '{cell.Content}'.");
    }
}
Extract the layout of a document from a file stream
To extract the layout from a given file at a file stream, use the AnalyzeDocument method and pass prebuilt-layout as the model ID. The returned value is an AnalyzeResult object containing data about the submitted document.

string filePath = "<filePath>";
using var stream = new FileStream(filePath, FileMode.Open);

AnalyzeDocumentOperation operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-layout", stream);
AnalyzeResult result = operation.Value;

foreach (DocumentPage page in result.Pages)
{
    Console.WriteLine($"Document Page {page.PageNumber} has {page.Lines.Count} line(s), {page.Words.Count} word(s),");
    Console.WriteLine($"and {page.SelectionMarks.Count} selection mark(s).");

    for (int i = 0; i < page.Lines.Count; i++)
    {
        DocumentLine line = page.Lines[i];
        Console.WriteLine($"  Line {i} has content: '{line.Content}'.");

        Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");

        for (int j = 0; j < line.BoundingPolygon.Count; j++)
        {
            Console.WriteLine($"      Point {j} => X: {line.BoundingPolygon[j].X}, Y: {line.BoundingPolygon[j].Y}");
        }
    }

    for (int i = 0; i < page.SelectionMarks.Count; i++)
    {
        DocumentSelectionMark selectionMark = page.SelectionMarks[i];

        Console.WriteLine($"  Selection Mark {i} is {selectionMark.State}.");
        Console.WriteLine($"    Its bounding polygon (points ordered clockwise):");

        for (int j = 0; j < selectionMark.BoundingPolygon.Count; j++)
        {
            Console.WriteLine($"      Point {j} => X: {selectionMark.BoundingPolygon[j].X}, Y: {selectionMark.BoundingPolygon[j].Y}");
        }
    }
}

Console.WriteLine("Paragraphs:");

foreach (DocumentParagraph paragraph in result.Paragraphs)
{
    Console.WriteLine($"  Paragraph content: {paragraph.Content}");

    if (paragraph.Role != null)
    {
        Console.WriteLine($"    Role: {paragraph.Role}");
    }
}

foreach (DocumentStyle style in result.Styles)
{
    // Check the style and style confidence to see if text is handwritten.
    // Note that value '0.8' is used as an example.

    bool isHandwritten = style.IsHandwritten.HasValue && style.IsHandwritten == true;

    if (isHandwritten && style.Confidence > 0.8)
    {
        Console.WriteLine($"Handwritten content found:");

        foreach (DocumentSpan span in style.Spans)
        {
            Console.WriteLine($"  Content: {result.Content.Substring(span.Index, span.Length)}");
        }
    }
}

Console.WriteLine("The following tables were extracted:");

for (int i = 0; i < result.Tables.Count; i++)
{
    DocumentTable table = result.Tables[i];
    Console.WriteLine($"  Table {i} has {table.RowCount} rows and {table.ColumnCount} columns.");

    foreach (DocumentTableCell cell in table.Cells)
    {
        Console.WriteLine($"    Cell ({cell.RowIndex}, {cell.ColumnIndex}) has kind '{cell.Kind}' and content: '{cell.Content}'.");
    }
}

Kör ditt program

När du har lagt till ett kodexempel i programmet väljer du den gröna Start-knappen bredvid formRecognizer_quickstart för att skapa och köra programmet, eller tryck på F5.

Lägg till följande kodexempel i din Program.cs-fil. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

using Azure;
using Azure.AI.DocumentIntelligence;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `DocumentIntelligenceClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentIntelligenceClient client = new DocumentIntelligenceClient(new Uri(endpoint), credential);

//sample invoice document

Uri invoiceUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf");

AnalyzeDocumentContent content = new AnalyzeDocumentContent()
{
    UrlSource = invoiceUri
};

Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-invoice", content);

AnalyzeResult result = operation.Value;

for (int i = 0; i < result.Documents.Count; i++)
{
    Console.WriteLine($"Document {i}:");

    AnalyzedDocument document = result.Documents[i];

    if (document.Fields.TryGetValue("VendorName", out DocumentField vendorNameField)
        && vendorNameField.Type == DocumentFieldType.String)
    {
        string vendorName = vendorNameField.ValueString;
        Console.WriteLine($"Vendor Name: '{vendorName}', with confidence {vendorNameField.Confidence}");
    }

    if (document.Fields.TryGetValue("CustomerName", out DocumentField customerNameField)
        && customerNameField.Type == DocumentFieldType.String)
    {
        string customerName = customerNameField.ValueString;
        Console.WriteLine($"Customer Name: '{customerName}', with confidence {customerNameField.Confidence}");
    }

    if (document.Fields.TryGetValue("Items", out DocumentField itemsField)
        && itemsField.Type == DocumentFieldType.Array)
    {
        foreach (DocumentField itemField in itemsField.ValueArray)
        {
            Console.WriteLine("Item:");

            if (itemField.Type == DocumentFieldType.Object)
            {
                IReadOnlyDictionary<string, DocumentField> itemFields = itemField.ValueObject;

                if (itemFields.TryGetValue("Description", out DocumentField itemDescriptionField)
                    && itemDescriptionField.Type == DocumentFieldType.String)
                {
                    string itemDescription = itemDescriptionField.ValueString;
                    Console.WriteLine($"  Description: '{itemDescription}', with confidence {itemDescriptionField.Confidence}");
                }

                if (itemFields.TryGetValue("Amount", out DocumentField itemAmountField)
                    && itemAmountField.Type == DocumentFieldType.Currency)
                {
                    CurrencyValue itemAmount = itemAmountField.ValueCurrency;
                    Console.WriteLine($"  Amount: '{itemAmount.CurrencySymbol}{itemAmount.Amount}', with confidence {itemAmountField.Confidence}");
                }
            }
        }
    }

    if (document.Fields.TryGetValue("SubTotal", out DocumentField subTotalField)
        && subTotalField.Type == DocumentFieldType.Currency)
    {
        CurrencyValue subTotal = subTotalField.ValueCurrency;
        Console.WriteLine($"Sub Total: '{subTotal.CurrencySymbol}{subTotal.Amount}', with confidence {subTotalField.Confidence}");
    }

    if (document.Fields.TryGetValue("TotalTax", out DocumentField totalTaxField)
        && totalTaxField.Type == DocumentFieldType.Currency)
    {
        CurrencyValue totalTax = totalTaxField.ValueCurrency;
        Console.WriteLine($"Total Tax: '{totalTax.CurrencySymbol}{totalTax.Amount}', with confidence {totalTaxField.Confidence}");
    }

    if (document.Fields.TryGetValue("InvoiceTotal", out DocumentField invoiceTotalField)
        && invoiceTotalField.Type == DocumentFieldType.Currency)
    {
        CurrencyValue invoiceTotal = invoiceTotalField.ValueCurrency;
        Console.WriteLine($"Invoice Total: '{invoiceTotal.CurrencySymbol}{invoiceTotal.Amount}', with confidence {invoiceTotalField.Confidence}");
    }
}

Kör ditt program

När du har lagt till ett kodexempel i programmet väljer du den gröna Start-knappen bredvid formRecognizer_quickstart för att skapa och köra programmet, eller tryck på F5.

Lägg till följande kodexempel i din Program.cs-fil. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

using Azure;
using Azure.AI.FormRecognizer.DocumentAnalysis;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `FormRecognizerClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentAnalysisClient client = new DocumentAnalysisClient(new Uri(endpoint), credential);

//sample invoice document

Uri invoiceUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf");

Operation operation = await client.AnalyzeDocumentAsync(WaitUntil.Completed, "prebuilt-invoice", invoiceUri);

AnalyzeResult result = operation.Value;

for (int i = 0; i < result.Documents.Count; i++)
{
    Console.WriteLine($"Document {i}:");

    AnalyzedDocument document = result.Documents[i];

    if (document.Fields.TryGetValue("VendorName", out DocumentField vendorNameField))
    {
        if (vendorNameField.FieldType == DocumentFieldType.String)
        {
            string vendorName = vendorNameField.Value.AsString();
            Console.WriteLine($"Vendor Name: '{vendorName}', with confidence {vendorNameField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("CustomerName", out DocumentField customerNameField))
    {
        if (customerNameField.FieldType == DocumentFieldType.String)
        {
            string customerName = customerNameField.Value.AsString();
            Console.WriteLine($"Customer Name: '{customerName}', with confidence {customerNameField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("Items", out DocumentField itemsField))
    {
        if (itemsField.FieldType == DocumentFieldType.List)
        {
            foreach (DocumentField itemField in itemsField.Value.AsList())
            {
                Console.WriteLine("Item:");

                if (itemField.FieldType == DocumentFieldType.Dictionary)
                {
                    IReadOnlyDictionary<string, DocumentField> itemFields = itemField.Value.AsDictionary();

                    if (itemFields.TryGetValue("Description", out DocumentField itemDescriptionField))
                    {
                        if (itemDescriptionField.FieldType == DocumentFieldType.String)
                        {
                            string itemDescription = itemDescriptionField.Value.AsString();

                            Console.WriteLine($"  Description: '{itemDescription}', with confidence {itemDescriptionField.Confidence}");
                        }
                    }

                    if (itemFields.TryGetValue("Amount", out DocumentField itemAmountField))
                    {
                        if (itemAmountField.FieldType == DocumentFieldType.Currency)
                        {
                            CurrencyValue itemAmount = itemAmountField.Value.AsCurrency();

                            Console.WriteLine($"  Amount: '{itemAmount.Symbol}{itemAmount.Amount}', with confidence {itemAmountField.Confidence}");
                        }
                    }
                }
            }
        }
    }

    if (document.Fields.TryGetValue("SubTotal", out DocumentField subTotalField))
    {
        if (subTotalField.FieldType == DocumentFieldType.Currency)
        {
            CurrencyValue subTotal = subTotalField.Value.AsCurrency();
            Console.WriteLine($"Sub Total: '{subTotal.Symbol}{subTotal.Amount}', with confidence {subTotalField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("TotalTax", out DocumentField totalTaxField))
    {
        if (totalTaxField.FieldType == DocumentFieldType.Currency)
        {
            CurrencyValue totalTax = totalTaxField.Value.AsCurrency();
            Console.WriteLine($"Total Tax: '{totalTax.Symbol}{totalTax.Amount}', with confidence {totalTaxField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("InvoiceTotal", out DocumentField invoiceTotalField))
    {
        if (invoiceTotalField.FieldType == DocumentFieldType.Currency)
        {
            CurrencyValue invoiceTotal = invoiceTotalField.Value.AsCurrency();
            Console.WriteLine($"Invoice Total: '{invoiceTotal.Symbol}{invoiceTotal.Amount}', with confidence {invoiceTotalField.Confidence}");
        }
    }
}

Kör ditt program

När du har lagt till ett kodexempel i programmet väljer du den gröna Start-knappen bredvid formRecognizer_quickstart för att skapa och köra programmet, eller tryck på F5.

Fördefinierade modellutdata

Här är ett kodfragment av förväntade utdata:

  Document 0:
  Vendor Name: 'CONTOSO LTD.', with confidence 0.962
  Customer Name: 'MICROSOFT CORPORATION', with confidence 0.951
  Item:
    Description: 'Test for 23 fields', with confidence 0.899
    Amount: '100', with confidence 0.902
  Sub Total: '100', with confidence 0.979

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa de fördefinierade fakturamodellutdata.

Lägg till följande kodexempel i din Program.cs-fil. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

using Azure;
using Azure.AI.FormRecognizer.DocumentAnalysis;

//set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal to create your `AzureKeyCredential` and `FormRecognizerClient` instance
string endpoint = "<your-endpoint>";
string key = "<your-key>";
AzureKeyCredential credential = new AzureKeyCredential(key);
DocumentAnalysisClient client = new DocumentAnalysisClient(new Uri(endpoint), credential);

//sample invoice document

Uri invoiceUri = new Uri ("https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf");

AnalyzeDocumentOperation operation = await client.AnalyzeDocumentFromUriAsync(WaitUntil.Completed, "prebuilt-invoice", invoiceUri);

AnalyzeResult result = operation.Value;

for (int i = 0; i < result.Documents.Count; i++)
{
    Console.WriteLine($"Document {i}:");

    AnalyzedDocument document = result.Documents[i];

    if (document.Fields.TryGetValue("VendorName", out DocumentField vendorNameField))
    {
        if (vendorNameField.FieldType == DocumentFieldType.String)
        {
            string vendorName = vendorNameField.Value.AsString();
            Console.WriteLine($"Vendor Name: '{vendorName}', with confidence {vendorNameField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("CustomerName", out DocumentField customerNameField))
    {
        if (customerNameField.FieldType == DocumentFieldType.String)
        {
            string customerName = customerNameField.Value.AsString();
            Console.WriteLine($"Customer Name: '{customerName}', with confidence {customerNameField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("Items", out DocumentField itemsField))
    {
        if (itemsField.FieldType == DocumentFieldType.List)
        {
            foreach (DocumentField itemField in itemsField.Value.AsList())
            {
                Console.WriteLine("Item:");

                if (itemField.FieldType == DocumentFieldType.Dictionary)
                {
                    IReadOnlyDictionary<string, DocumentField> itemFields = itemField.Value.AsDictionary();

                    if (itemFields.TryGetValue("Description", out DocumentField itemDescriptionField))
                    {
                        if (itemDescriptionField.FieldType == DocumentFieldType.String)
                        {
                            string itemDescription = itemDescriptionField.Value.AsString();

                            Console.WriteLine($"  Description: '{itemDescription}', with confidence {itemDescriptionField.Confidence}");
                        }
                    }

                    if (itemFields.TryGetValue("Amount", out DocumentField itemAmountField))
                    {
                        if (itemAmountField.FieldType == DocumentFieldType.Currency)
                        {
                            CurrencyValue itemAmount = itemAmountField.Value.AsCurrency();

                            Console.WriteLine($"  Amount: '{itemAmount.Symbol}{itemAmount.Amount}', with confidence {itemAmountField.Confidence}");
                        }
                    }
                }
            }
        }
    }

    if (document.Fields.TryGetValue("SubTotal", out DocumentField subTotalField))
    {
        if (subTotalField.FieldType == DocumentFieldType.Currency)
        {
            CurrencyValue subTotal = subTotalField.Value.AsCurrency();
            Console.WriteLine($"Sub Total: '{subTotal.Symbol}{subTotal.Amount}', with confidence {subTotalField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("TotalTax", out DocumentField totalTaxField))
    {
        if (totalTaxField.FieldType == DocumentFieldType.Currency)
        {
            CurrencyValue totalTax = totalTaxField.Value.AsCurrency();
            Console.WriteLine($"Total Tax: '{totalTax.Symbol}{totalTax.Amount}', with confidence {totalTaxField.Confidence}");
        }
    }

    if (document.Fields.TryGetValue("InvoiceTotal", out DocumentField invoiceTotalField))
    {
        if (invoiceTotalField.FieldType == DocumentFieldType.Currency)
        {
            CurrencyValue invoiceTotal = invoiceTotalField.Value.AsCurrency();
            Console.WriteLine($"Invoice Total: '{invoiceTotal.Symbol}{invoiceTotal.Amount}', with confidence {invoiceTotalField.Confidence}");
        }
    }
}

Kör ditt program

När du har lagt till ett kodexempel i programmet väljer du den gröna Start-knappen bredvid formRecognizer_quickstart för att skapa och köra programmet, eller tryck på F5.

Klientbibliotekets SDK-referens | REST API-referenspaket | (Maven) | Exempel |som stöds REST API-version |

Klientbibliotekets SDK-referens | REST API-referenspaket | (Maven) | Exempel| som stöds REST API-version |

Klientbibliotekets SDK-referens | REST API-referenspaket | (Maven) | Exempel|som stöds REST API-version |

1. I konsolfönstret (till exempel cmd, PowerShell eller Bash) skapar du en ny katalog för din app med namnet doc-intel-app och navigerar till den.

   mkdir doc-intel-app && doc-intel-app
   

   mkdir doc-intel-app; cd doc-intel-app
   

2. Kör kommandot från arbetskatalogen gradle init . Det här kommandot skapar viktiga byggfiler för Gradle, inklusive build.gradle.kts, som används vid körning för att skapa och konfigurera ditt program.

   gradle init --type basic
   

3. Välj en DSL när du uppmanas till det och välj Kotlin.

4. Acceptera standardprojektets namn (doc-intel-app) genom att välja Retur eller Retur.

1. I konsolfönstret (till exempel cmd, PowerShell eller Bash) skapar du en ny katalog för din app med namnet formigenkänningsapp och navigerar till den.

   mkdir form-recognize-app && form-recognize-app
   

   mkdir form-recognize-app; cd form-recognize-app
   

2. Kör kommandot från arbetskatalogen gradle init . Det här kommandot skapar viktiga byggfiler för Gradle, inklusive build.gradle.kts, som används vid körning för att skapa och konfigurera ditt program.

   gradle init --type basic
   

3. Välj en DSL när du uppmanas till det och välj Kotlin.

4. Acceptera standardprojektets namn (formigenkänningsapp) genom att välja Retur eller Retur.

Den här snabbstarten använder Gradle-beroendehanteraren. Du hittar klientbiblioteket och information för andra beroendehanterare på Den centrala Maven-lagringsplatsen.

Öppna projektets build.gradle.kts-fil i din IDE. Copay och förbi följande kod för att inkludera klientbiblioteket som en implementation instruktion, tillsammans med nödvändiga plugin-program och inställningar.

   plugins {
       java
       application
   }
   application {
       mainClass.set("DocIntelligence")
   }
   repositories {
       mavenCentral()
   }
   dependencies {
       implementation group: 'com.azure', name: 'azure-ai-documentintelligence', version: '1.0.0-beta.4'

   }

Den här snabbstarten använder Gradle-beroendehanteraren. Du hittar klientbiblioteket och information för andra beroendehanterare på Den centrala Maven-lagringsplatsen.

Öppna projektets build.gradle.kts-fil i din IDE. Copay och förbi följande kod för att inkludera klientbiblioteket som en implementation instruktion, tillsammans med nödvändiga plugin-program och inställningar.

   plugins {
       java
       application
   }
   application {
       mainClass.set("FormRecognizer")
   }
   repositories {
       mavenCentral()
   }
   dependencies {
       implementation group: 'com.azure', name: 'azure-ai-formrecognizer', version: '4.1.0'

   }

Den här snabbstarten använder Gradle-beroendehanteraren. Du hittar klientbiblioteket och information för andra beroendehanterare på Den centrala Maven-lagringsplatsen.

Öppna projektets build.gradle.kts-fil i din IDE. Copay och förbi följande kod för att inkludera klientbiblioteket som en implementation instruktion, tillsammans med nödvändiga plugin-program och inställningar.

   plugins {
       java
       application
   }
   application {
       mainClass.set("FormRecognizer")
   }
   repositories {
       mavenCentral()
   }
   dependencies {
       implementation group: 'com.azure', name: 'azure-ai-formrecognizer', version: '4.0.0'


   }

Om du vill interagera med document intelligence-tjänsten måste du skapa en instans av DocumentIntelligenceClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentIntelligenceClient instans med och dokumentinformationen AzureKeyCredential endpoint.

Om du vill interagera med document intelligence-tjänsten måste du skapa en instans av DocumentAnalysisClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentAnalysisClient instans med och dokumentinformationen AzureKeyCredential endpoint.

2. Navigera till java katalogen och skapa en fil med namnet DocIntelligence.java.

   Dricks

   • Du kan skapa en ny fil med hjälp av PowerShell.
   • Öppna ett PowerShell-fönster i projektkatalogen genom att hålla ned Skift-tangenten och högerklicka på mappen.
   • Skriv följande kommando New-Item DocIntelligence.java.

3. Öppna DocIntelligence.java-filen. Kopiera och klistra in något av följande kodexempel i ditt program:

   • Layout

   • Fördefinierad faktura

1. Navigera till java katalogen och skapa en fil med namnet FormRecognizer.java.

   Dricks

   • Du kan skapa en ny fil med hjälp av PowerShell.
   • Öppna ett PowerShell-fönster i projektkatalogen genom att hålla ned Skift-tangenten och högerklicka på mappen.
   • Skriv följande kommando new-item FormRecognizer.java.

2. Öppna FormRecognizer.java-filen. Kopiera och klistra in något av följande kodexempel i ditt program:

   • Layout

   • Fördefinierad faktura

Lägg till följande kodexempel i DocIntelligence.java filen. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

import com.azure.ai.documentintelligence.models.AnalyzeDocumentRequest;
import com.azure.ai.documentintelligence.models.AnalyzeResult;
import com.azure.ai.documentintelligence.models.AnalyzeResultOperation;
import com.azure.ai.documentintelligence.models.DocumentTable;
import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.util.List;

public class DocIntelligence {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(String[] args) {

    // create your `DocumentIntelligenceClient` instance and `AzureKeyCredential` variable
    DocumentIntelligenceClient client = new DocumentIntelligenceClientBuilder()
      .credential(new AzureKeyCredential(key))
      .endpoint(endpoint)
      .buildClient();

    // sample document
    String modelId = "prebuilt-layout";
    String documentUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf";

    SyncPoller <AnalyzeResultOperation, AnalyzeResultOperation> analyzeLayoutPoller =
      client.beginAnalyzeDocument(modelId,
          null,
          null,
          null,
          null,
          null,
          null,
          new AnalyzeDocumentRequest().setUrlSource(documentUrl));

    AnalyzeResult analyzeLayoutResult = analyzeLayoutPoller.getFinalResult().getAnalyzeResult();

    // pages
    analyzeLayoutResult.getPages().forEach(documentPage -> {
      System.out.printf("Page has width: %.2f and height: %.2f, measured with unit: %s%n",
        documentPage.getWidth(),
        documentPage.getHeight(),
        documentPage.getUnit());

      // lines
      documentPage.getLines().forEach(documentLine ->
        System.out.printf("Line '%s' is within a bounding polygon %s.%n",
          documentLine.getContent(),
          documentLine.getPolygon()));

      // words
      documentPage.getWords().forEach(documentWord ->
        System.out.printf("Word '%s' has a confidence score of %.2f.%n",
          documentWord.getContent(),
          documentWord.getConfidence()));

      // selection marks
      documentPage.getSelectionMarks().forEach(documentSelectionMark ->
        System.out.printf("Selection mark is '%s' and is within a bounding polygon %s with confidence %.2f.%n",
          documentSelectionMark.getState().toString(),
          documentSelectionMark.getPolygon(),
          documentSelectionMark.getConfidence()));
    });

    // tables
    List < DocumentTable > tables = analyzeLayoutResult.getTables();
    for (int i = 0; i < tables.size(); i++) {
      DocumentTable documentTable = tables.get(i);
      System.out.printf("Table %d has %d rows and %d columns.%n", i, documentTable.getRowCount(),
        documentTable.getColumnCount());
      documentTable.getCells().forEach(documentTableCell -> {
        System.out.printf("Cell '%s', has row index %d and column index %d.%n", documentTableCell.getContent(),
          documentTableCell.getRowIndex(), documentTableCell.getColumnIndex());
      });
      System.out.println();
    }

    // styles
    analyzeLayoutResult.getStyles().forEach(documentStyle -
      > System.out.printf("Document is handwritten %s.%n", documentStyle.isHandwritten()));
  }
}

Skapa och köra programmet

När du har lagt till ett kodexempel i programmet går du tillbaka till huvudprojektkatalogen – doc-intel-app.

1. Skapa ditt program med build kommandot :

   gradle build
   

2. Kör programmet med run kommandot :

   gradle run
   

Lägg till följande kodexempel i FormRecognizer.java filen. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

import com.azure.ai.formrecognizer.documentanalysis.models.*;
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClient;
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClientBuilder;

import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.io.IOException;
import java.util.List;
import java.util.Arrays;
import java.time.LocalDate;
import java.util.Map;
import java.util.stream.Collectors;

public class FormRecognizer {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(String[] args) {

    // create your `DocumentAnalysisClient` instance and `AzureKeyCredential` variable
    DocumentAnalysisClient client = new DocumentAnalysisClientBuilder()
      .credential(new AzureKeyCredential(key))
      .endpoint(endpoint)
      .buildClient();

    // sample document
    String documentUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf";
    String modelId = "prebuilt-layout";

    SyncPoller < OperationResult, AnalyzeResult > analyzeLayoutResultPoller =
      client.beginAnalyzeDocumentFromUrl(modelId, documentUrl);

    AnalyzeResult analyzeLayoutResult = analyzeLayoutResultPoller.getFinalResult();

    // pages
    analyzeLayoutResult.getPages().forEach(documentPage -> {
      System.out.printf("Page has width: %.2f and height: %.2f, measured with unit: %s%n",
        documentPage.getWidth(),
        documentPage.getHeight(),
        documentPage.getUnit());

      // lines
      documentPage.getLines().forEach(documentLine ->
        System.out.printf("Line %s is within a bounding polygon %s.%n",
          documentLine.getContent(),
          documentLine.getBoundingPolygon().toString()));

      // words
      documentPage.getWords().forEach(documentWord ->
        System.out.printf("Word '%s' has a confidence score of %.2f%n",
          documentWord.getContent(),
          documentWord.getConfidence()));

      // selection marks
      documentPage.getSelectionMarks().forEach(documentSelectionMark ->
        System.out.printf("Selection mark is %s and is within a bounding polygon %s with confidence %.2f.%n",
          documentSelectionMark.getState().toString(),
          documentSelectionMark.getBoundingPolygon().toString(),
          documentSelectionMark.getConfidence()));
    });

    // tables
    List < DocumentTable > tables = analyzeLayoutResult.getTables();
    for (int i = 0; i < tables.size(); i++) {
      DocumentTable documentTable = tables.get(i);
      System.out.printf("Table %d has %d rows and %d columns.%n", i, documentTable.getRowCount(),
        documentTable.getColumnCount());
      documentTable.getCells().forEach(documentTableCell -> {
        System.out.printf("Cell '%s', has row index %d and column index %d.%n", documentTableCell.getContent(),
          documentTableCell.getRowIndex(), documentTableCell.getColumnIndex());
      });
      System.out.println();
    }
  }
  // Utility function to get the bounding polygon coordinates
  private static String getBoundingCoordinates(List < Point > boundingPolygon) {
    return boundingPolygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
      point.getY())).collect(Collectors.joining(", "));
  }
}

Skapa och köra programmet

När du har lagt till ett kodexempel i programmet går du tillbaka till huvudprojektkatalogen – formigenkänningsapp.

1. Skapa ditt program med build kommandot :

   gradle build
   

2. Kör programmet med run kommandot :

   gradle run
   

Utdata för layoutmodell

Här är ett kodfragment av förväntade utdata:

  Table 0 has 5 rows and 3 columns.
  Cell 'Title of each class', has row index 0 and column index 0.
  Cell 'Trading Symbol', has row index 0 and column index 1.
  Cell 'Name of exchange on which registered', has row index 0 and column index 2.
  Cell 'Common stock, $0.00000625 par value per share', has row index 1 and column index 0.
  Cell 'MSFT', has row index 1 and column index 1.
  Cell 'NASDAQ', has row index 1 and column index 2.
  Cell '2.125% Notes due 2021', has row index 2 and column index 0.
  Cell 'MSFT', has row index 2 and column index 1.
  Cell 'NASDAQ', has row index 2 and column index 2.
  Cell '3.125% Notes due 2028', has row index 3 and column index 0.
  Cell 'MSFT', has row index 3 and column index 1.
  Cell 'NASDAQ', has row index 3 and column index 2.
  Cell '2.625% Notes due 2033', has row index 4 and column index 0.
  Cell 'MSFT', has row index 4 and column index 1.
  Cell 'NASDAQ', has row index 4 and column index 2.

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa layoutmodellens utdata.

Lägg till följande kodexempel i FormRecognizer.java filen. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClient;
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClientBuilder;
import com.azure.ai.formrecognizer.documentanalysis.models.AnalyzeResult;
import com.azure.ai.formrecognizer.documentanalysis.models.OperationResult;
import com.azure.ai.formrecognizer.documentanalysis.models.DocumentTable;
import com.azure.ai.formrecognizer.documentanalysis.models.Point;
import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.util.List;
import java.util.stream.Collectors;

public class FormRecognizer {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(String[] args) {

    // create your `DocumentAnalysisClient` instance and `AzureKeyCredential` variable
    DocumentAnalysisClient client = new DocumentAnalysisClientBuilder()
      .credential(new AzureKeyCredential(key))
      .endpoint(endpoint)
      .buildClient();

    // sample document
    String documentUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf";
    String modelId = "prebuilt-layout";

    SyncPoller < OperationResult, AnalyzeResult > analyzeLayoutPoller =
      client.beginAnalyzeDocumentFromUrl(modelId, documentUrl);

    AnalyzeResult analyzeLayoutResult = analyzeLayoutPoller.getFinalResult();

    // pages
    analyzeLayoutResult.getPages().forEach(documentPage -> {
      System.out.printf("Page has width: %.2f and height: %.2f, measured with unit: %s%n",
        documentPage.getWidth(),
        documentPage.getHeight(),
        documentPage.getUnit());

      // lines
      documentPage.getLines().forEach(documentLine ->
        System.out.printf("Line '%s' is within a bounding polygon %s.%n",
          documentLine.getContent(),
          getBoundingCoordinates(documentLine.getBoundingPolygon())));

      // words
      documentPage.getWords().forEach(documentWord ->
        System.out.printf("Word '%s' has a confidence score of %.2f.%n",
          documentWord.getContent(),
          documentWord.getConfidence()));

      // selection marks
      documentPage.getSelectionMarks().forEach(documentSelectionMark ->
        System.out.printf("Selection mark is '%s' and is within a bounding polygon %s with confidence %.2f.%n",
          documentSelectionMark.getSelectionMarkState().toString(),
          getBoundingCoordinates(documentSelectionMark.getBoundingPolygon()),
          documentSelectionMark.getConfidence()));
    });

    // tables
    List < DocumentTable > tables = analyzeLayoutResult.getTables();
    for (int i = 0; i < tables.size(); i++) {
      DocumentTable documentTable = tables.get(i);
      System.out.printf("Table %d has %d rows and %d columns.%n", i, documentTable.getRowCount(),
        documentTable.getColumnCount());
      documentTable.getCells().forEach(documentTableCell -> {
        System.out.printf("Cell '%s', has row index %d and column index %d.%n", documentTableCell.getContent(),
          documentTableCell.getRowIndex(), documentTableCell.getColumnIndex());
      });
      System.out.println();
    }

    // styles
    analyzeLayoutResult.getStyles().forEach(documentStyle -
      > System.out.printf("Document is handwritten %s.%n", documentStyle.isHandwritten()));
  }

  /**
   * Utility function to get the bounding polygon coordinates.
   */
  private static String getBoundingCoordinates(List < Point > boundingPolygon) {
    return boundingPolygon.stream().map(point -> String.format("[%.2f, %.2f]", point.getX(),
      point.getY())).collect(Collectors.joining(", "));
  }
}

Skapa och köra programmet

När du har lagt till ett kodexempel i programmet går du tillbaka till huvudprojektkatalogen – formigenkänningsapp.

1. Skapa ditt program med build kommandot :

   gradle build
   

2. Kör programmet med run kommandot :

   gradle run
   

Lägg till följande kodexempel i DocIntelligence.java filen. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

import com.azure.ai.documentintelligence.models.AnalyzeDocumentRequest;
import com.azure.ai.documentintelligence.models.AnalyzeResult;
import com.azure.ai.documentintelligence.models.AnalyzeResultOperation;
import com.azure.ai.documentintelligence.models.Document;
import com.azure.ai.documentintelligence.models.DocumentField;
import com.azure.ai.documentintelligence.models.DocumentFieldType;
import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.io.IOException;
import java.time.LocalDate;
import java.util.List;
import java.util.Map;

public class DocIntelligence {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(String[] args) {

    // sample document
    String modelId = "prebuilt-invoice";
    String invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf";

    public static void main(final String[] args) throws IOException {

      // Instantiate a client that will be used to call the service.
      DocumentIntelligenceClient client = new DocumentIntelligenceClientBuilder()
        .credential(new AzureKeyCredential(key))
        .endpoint(endpoint)
        .buildClient();

      SyncPoller<AnalyzeResultOperation, AnalyzeResultOperation > analyzeInvoicesPoller =
        client.beginAnalyzeDocument(modelId, 
            null,
            null,
            null,
            null,
            null,
            null,
            new AnalyzeDocumentRequest().setUrlSource(invoiceUrl));

      AnalyzeResult analyzeInvoiceResult = analyzeInvoicesPoller.getFinalResult().getAnalyzeResult();

      for (int i = 0; i < analyzeInvoiceResult.getDocuments().size(); i++) {
        Document analyzedInvoice = analyzeInvoiceResult.getDocuments().get(i);
        Map < String, DocumentField > invoiceFields = analyzedInvoice.getFields();
        System.out.printf("----------- Analyzing invoice  %d -----------%n", i);
        DocumentField vendorNameField = invoiceFields.get("VendorName");
        if (vendorNameField != null) {
          if (DocumentFieldType.STRING == vendorNameField.getType()) {
            String merchantName = vendorNameField.getValueString();
            System.out.printf("Vendor Name: %s, confidence: %.2f%n",
              merchantName, vendorNameField.getConfidence());
          }
        }

        DocumentField vendorAddressField = invoiceFields.get("VendorAddress");
        if (vendorAddressField != null) {
          if (DocumentFieldType.STRING == vendorAddressField.getType()) {
            String merchantAddress = vendorAddressField.getValueString();
            System.out.printf("Vendor address: %s, confidence: %.2f%n",
              merchantAddress, vendorAddressField.getConfidence());
          }
        }

        DocumentField customerNameField = invoiceFields.get("CustomerName");
        if (customerNameField != null) {
          if (DocumentFieldType.STRING == customerNameField.getType()) {
            String merchantAddress = customerNameField.getValueString();
            System.out.printf("Customer Name: %s, confidence: %.2f%n",
              merchantAddress, customerNameField.getConfidence());
          }
        }

        DocumentField customerAddressRecipientField = invoiceFields.get("CustomerAddressRecipient");
        if (customerAddressRecipientField != null) {
          if (DocumentFieldType.STRING == customerAddressRecipientField.getType()) {
            String customerAddr = customerAddressRecipientField.getValueString();
            System.out.printf("Customer Address Recipient: %s, confidence: %.2f%n",
              customerAddr, customerAddressRecipientField.getConfidence());
          }
        }

        DocumentField invoiceIdField = invoiceFields.get("InvoiceId");
        if (invoiceIdField != null) {
          if (DocumentFieldType.STRING == invoiceIdField.getType()) {
            String invoiceId = invoiceIdField.getValueString();
            System.out.printf("Invoice ID: %s, confidence: %.2f%n",
              invoiceId, invoiceIdField.getConfidence());
          }
        }

        DocumentField invoiceDateField = invoiceFields.get("InvoiceDate");
        if (customerNameField != null) {
          if (DocumentFieldType.DATE == invoiceDateField.getType()) {
            LocalDate invoiceDate = invoiceDateField.getValueDate();
            System.out.printf("Invoice Date: %s, confidence: %.2f%n",
              invoiceDate, invoiceDateField.getConfidence());
          }
        }

        DocumentField invoiceTotalField = invoiceFields.get("InvoiceTotal");
        if (customerAddressRecipientField != null) {
          if (DocumentFieldType.NUMBER == invoiceTotalField.getType()) {
            Double invoiceTotal = invoiceTotalField.getValueNumber();
            System.out.printf("Invoice Total: %.2f, confidence: %.2f%n",
              invoiceTotal, invoiceTotalField.getConfidence());
          }
        }

        DocumentField invoiceItemsField = invoiceFields.get("Items");
        if (invoiceItemsField != null) {
          System.out.printf("Invoice Items: %n");
          if (DocumentFieldType.ARRAY == invoiceItemsField.getType()) {
            List < DocumentField > invoiceItems = invoiceItemsField.getValueArray();
            invoiceItems.stream()
              .filter(invoiceItem -> DocumentFieldType.OBJECT == invoiceItem.getType())
              .map(documentField -> documentField.getValueObject())
              .forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {

                // See a full list of fields found on an invoice here:
                // https://aka.ms/documentintelligence/invoicefields

                if ("Description".equals(key)) {
                  if (DocumentFieldType.STRING == documentField.getType()) {
                    String name = documentField.getValueString();
                    System.out.printf("Description: %s, confidence: %.2fs%n",
                      name, documentField.getConfidence());
                  }
                }
                if ("Quantity".equals(key)) {
                  if (DocumentFieldType.NUMBER == documentField.getType()) {
                    Double quantity = documentField.getValueNumber();
                    System.out.printf("Quantity: %f, confidence: %.2f%n",
                      quantity, documentField.getConfidence());
                  }
                }
                if ("UnitPrice".equals(key)) {
                  if (DocumentFieldType.NUMBER == documentField.getType()) {
                    Double unitPrice = documentField.getValueNumber();
                    System.out.printf("Unit Price: %f, confidence: %.2f%n",
                      unitPrice, documentField.getConfidence());
                  }
                }
                if ("ProductCode".equals(key)) {
                  if (DocumentFieldType.NUMBER == documentField.getType()) {
                    Double productCode = documentField.getValueNumber();
                    System.out.printf("Product Code: %f, confidence: %.2f%n",
                      productCode, documentField.getConfidence());
                  }
                }
              }));
          }
        }
      }
    }
  }
}

Skapa och köra programmet

När du har lagt till ett kodexempel i programmet går du tillbaka till huvudprojektkatalogen – doc-intel-app.

1. Skapa ditt program med build kommandot :

   gradle build
   

2. Kör programmet med run kommandot :

   gradle run
   

Lägg till följande kodexempel i FormRecognizer.java filen. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

import com.azure.ai.formrecognizer.documentanalysis.models.*;
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClient;
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClientBuilder;

import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.io.IOException;
import java.util.List;
import java.util.Arrays;
import java.time.LocalDate;
import java.util.Map;
import java.util.stream.Collectors;

public class FormRecognizer {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(final String[] args) throws IOException {

    // create your `DocumentAnalysisClient` instance and `AzureKeyCredential` variable
    DocumentAnalysisClient client = new DocumentAnalysisClientBuilder()
      .credential(new AzureKeyCredential(key))
      .endpoint(endpoint)
      .buildClient();

    // sample document
    String modelId = "prebuilt-invoice";
    String invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf";

    SyncPoller < OperationResult, AnalyzeResult > analyzeInvoicePoller = client.beginAnalyzeDocumentFromUrl(modelId, invoiceUrl);

    AnalyzeResult analyzeInvoiceResult = analyzeInvoicePoller.getFinalResult();

    for (int i = 0; i < analyzeInvoiceResult.getDocuments().size(); i++) {
      AnalyzedDocument analyzedInvoice = analyzeInvoiceResult.getDocuments().get(i);
      Map < String, DocumentField > invoiceFields = analyzedInvoice.getFields();
      System.out.printf("----------- Analyzing invoice  %d -----------%n", i);
      DocumentField vendorNameField = invoiceFields.get("VendorName");
      if (vendorNameField != null) {
        if (DocumentFieldType.STRING == vendorNameField.getType()) {
          String merchantName = vendorNameField.getValueAsString();
          System.out.printf("Vendor Name: %s, confidence: %.2f%n",
            merchantName, vendorNameField.getConfidence());
        }
      }

      DocumentField vendorAddressField = invoiceFields.get("VendorAddress");
      if (vendorAddressField != null) {
        if (DocumentFieldType.STRING == vendorAddressField.getType()) {
          String merchantAddress = vendorAddressField.getValueAsString();
          System.out.printf("Vendor address: %s, confidence: %.2f%n",
            merchantAddress, vendorAddressField.getConfidence());
        }
      }

      DocumentField customerNameField = invoiceFields.get("CustomerName");
      if (customerNameField != null) {
        if (DocumentFieldType.STRING == customerNameField.getType()) {
          String merchantAddress = customerNameField.getValueAsString();
          System.out.printf("Customer Name: %s, confidence: %.2f%n",
            merchantAddress, customerNameField.getConfidence());
        }
      }

      DocumentField customerAddressRecipientField = invoiceFields.get("CustomerAddressRecipient");
      if (customerAddressRecipientField != null) {
        if (DocumentFieldType.STRING == customerAddressRecipientField.getType()) {
          String customerAddr = customerAddressRecipientField.getValueAsString();
          System.out.printf("Customer Address Recipient: %s, confidence: %.2f%n",
            customerAddr, customerAddressRecipientField.getConfidence());
        }
      }

      DocumentField invoiceIdField = invoiceFields.get("InvoiceId");
      if (invoiceIdField != null) {
        if (DocumentFieldType.STRING == invoiceIdField.getType()) {
          String invoiceId = invoiceIdField.getValueAsString();
          System.out.printf("Invoice ID: %s, confidence: %.2f%n",
            invoiceId, invoiceIdField.getConfidence());
        }
      }

      DocumentField invoiceDateField = invoiceFields.get("InvoiceDate");
      if (customerNameField != null) {
        if (DocumentFieldType.DATE == invoiceDateField.getType()) {
          LocalDate invoiceDate = invoiceDateField.getValueAsDate();
          System.out.printf("Invoice Date: %s, confidence: %.2f%n",
            invoiceDate, invoiceDateField.getConfidence());
        }
      }

      DocumentField invoiceTotalField = invoiceFields.get("InvoiceTotal");
      if (customerAddressRecipientField != null) {
        if (DocumentFieldType.DOUBLE == invoiceTotalField.getType()) {
          Double invoiceTotal = invoiceTotalField.getValueAsDouble();
          System.out.printf("Invoice Total: %.2f, confidence: %.2f%n",
            invoiceTotal, invoiceTotalField.getConfidence());
        }
      }

      DocumentField invoiceItemsField = invoiceFields.get("Items");
      if (invoiceItemsField != null) {
        System.out.printf("Invoice Items: %n");
        if (DocumentFieldType.LIST == invoiceItemsField.getType()) {
          List < DocumentField > invoiceItems = invoiceItemsField.getValueAsList();
          invoiceItems.stream()
            .filter(invoiceItem -> DocumentFieldType.MAP == invoiceItem.getType())
            .map(documentField -> documentField.getValueAsMap())
            .forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {

              // See a full list of fields found on an invoice here:
              // https://aka.ms/formrecognizer/invoicefields

              if ("Description".equals(key)) {
                if (DocumentFieldType.STRING == documentField.getType()) {
                  String name = documentField.getValueAsString();
                  System.out.printf("Description: %s, confidence: %.2fs%n",
                    name, documentField.getConfidence());
                }
              }
              if ("Quantity".equals(key)) {
                if (DocumentFieldType.DOUBLE == documentField.getType()) {
                  Double quantity = documentField.getValueAsDouble();
                  System.out.printf("Quantity: %f, confidence: %.2f%n",
                    quantity, documentField.getConfidence());
                }
              }
              if ("UnitPrice".equals(key)) {
                if (DocumentFieldType.DOUBLE == documentField.getType()) {
                  Double unitPrice = documentField.getValueAsDouble();
                  System.out.printf("Unit Price: %f, confidence: %.2f%n",
                    unitPrice, documentField.getConfidence());
                }
              }
              if ("ProductCode".equals(key)) {
                if (DocumentFieldType.DOUBLE == documentField.getType()) {
                  Double productCode = documentField.getValueAsDouble();
                  System.out.printf("Product Code: %f, confidence: %.2f%n",
                    productCode, documentField.getConfidence());
                }
              }
            }));
        }
      }
    }
  }
}

Skapa och köra programmet

När du har lagt till ett kodexempel i programmet går du tillbaka till huvudprojektkatalogen – doc-intel-app.

1. Skapa ditt program med build kommandot :

   gradle build
   

2. Kör programmet med run kommandot :

   gradle run
   

Fördefinierade modellutdata

Här är ett kodfragment av förväntade utdata:

  ----------- Analyzing invoice  0 -----------
  Analyzed document has doc type invoice with confidence : 1.00
  Vendor Name: CONTOSO LTD., confidence: 0.92
  Vendor address: 123 456th St New York, NY, 10001, confidence: 0.91
  Customer Name: MICROSOFT CORPORATION, confidence: 0.84
  Customer Address Recipient: Microsoft Corp, confidence: 0.92
  Invoice ID: INV-100, confidence: 0.97
  Invoice Date: 2019-11-15, confidence: 0.97

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa de fördefinierade fakturamodellutdata.

Lägg till följande kodexempel i FormRecognizer.java filen. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClient;
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClientBuilder;
import com.azure.ai.formrecognizer.documentanalysis.models.AnalyzeResult;
import com.azure.ai.formrecognizer.documentanalysis.models.AnalyzedDocument;
import com.azure.ai.formrecognizer.documentanalysis.models.DocumentField;
import com.azure.ai.formrecognizer.documentanalysis.models.DocumentFieldType;
import com.azure.ai.formrecognizer.documentanalysis.models.OperationResult;
import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.util.polling.SyncPoller;

import java.io.IOException;
import java.time.LocalDate;
import java.util.List;
import java.util.Map;

public class FormRecognizer {

  // set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
  private static final String endpoint = "<your-endpoint>";
  private static final String key = "<your-key>";

  public static void main(String[] args) {

    // create your `DocumentAnalysisClient` instance and `AzureKeyCredential` variable
    DocumentAnalysisClient client = new DocumentAnalysisClientBuilder()
      .credential(new AzureKeyCredential(key))
      .endpoint(endpoint)
      .buildClient();

    // sample document
    String modelId = "prebuilt-invoice";
    String invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf";

    SyncPoller < OperationResult, AnalyzeResult > analyzeInvoicePoller = client.beginAnalyzeDocumentFromUrl(modelId, invoiceUrl);

    AnalyzeResult analyzeInvoiceResult = analyzeInvoicePoller.getFinalResult();

    for (int i = 0; i < analyzeInvoiceResult.getDocuments().size(); i++) {
      AnalyzedDocument analyzedInvoice = analyzeInvoiceResult.getDocuments().get(i);
      Map < String, DocumentField > invoiceFields = analyzedInvoice.getFields();
      System.out.printf("----------- Analyzing invoice  %d -----------%n", i);
      DocumentField vendorNameField = invoiceFields.get("VendorName");
      if (vendorNameField != null) {
        if (DocumentFieldType.STRING == vendorNameField.getType()) {
          String merchantName = vendorNameField.getValueAsString();
          System.out.printf("Vendor Name: %s, confidence: %.2f%n",
            merchantName, vendorNameField.getConfidence());
        }
      }

      DocumentField vendorAddressField = invoiceFields.get("VendorAddress");
      if (vendorAddressField != null) {
        if (DocumentFieldType.STRING == vendorAddressField.getType()) {
          String merchantAddress = vendorAddressField.getValueAsString();
          System.out.printf("Vendor address: %s, confidence: %.2f%n",
            merchantAddress, vendorAddressField.getConfidence());
        }
      }

      DocumentField customerNameField = invoiceFields.get("CustomerName");
      if (customerNameField != null) {
        if (DocumentFieldType.STRING == customerNameField.getType()) {
          String merchantAddress = customerNameField.getValueAsString();
          System.out.printf("Customer Name: %s, confidence: %.2f%n",
            merchantAddress, customerNameField.getConfidence());
        }
      }

      DocumentField customerAddressRecipientField = invoiceFields.get("CustomerAddressRecipient");
      if (customerAddressRecipientField != null) {
        if (DocumentFieldType.STRING == customerAddressRecipientField.getType()) {
          String customerAddr = customerAddressRecipientField.getValueAsString();
          System.out.printf("Customer Address Recipient: %s, confidence: %.2f%n",
            customerAddr, customerAddressRecipientField.getConfidence());
        }
      }

      DocumentField invoiceIdField = invoiceFields.get("InvoiceId");
      if (invoiceIdField != null) {
        if (DocumentFieldType.STRING == invoiceIdField.getType()) {
          String invoiceId = invoiceIdField.getValueAsString();
          System.out.printf("Invoice ID: %s, confidence: %.2f%n",
            invoiceId, invoiceIdField.getConfidence());
        }
      }

      DocumentField invoiceDateField = invoiceFields.get("InvoiceDate");
      if (customerNameField != null) {
        if (DocumentFieldType.DATE == invoiceDateField.getType()) {
          LocalDate invoiceDate = invoiceDateField.getValueAsDate();
          System.out.printf("Invoice Date: %s, confidence: %.2f%n",
            invoiceDate, invoiceDateField.getConfidence());
        }
      }

      DocumentField invoiceTotalField = invoiceFields.get("InvoiceTotal");
      if (customerAddressRecipientField != null) {
        if (DocumentFieldType.DOUBLE == invoiceTotalField.getType()) {
          Double invoiceTotal = invoiceTotalField.getValueAsDouble();
          System.out.printf("Invoice Total: %.2f, confidence: %.2f%n",
            invoiceTotal, invoiceTotalField.getConfidence());
        }
      }

      DocumentField invoiceItemsField = invoiceFields.get("Items");
      if (invoiceItemsField != null) {
        System.out.printf("Invoice Items: %n");
        if (DocumentFieldType.LIST == invoiceItemsField.getType()) {
          List < DocumentField > invoiceItems = invoiceItemsField.getValueAsList();
          invoiceItems.stream()
            .filter(invoiceItem -> DocumentFieldType.MAP == invoiceItem.getType())
            .map(documentField -> documentField.getValueAsMap())
            .forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {

              // See a full list of fields found on an invoice here:
              // https://aka.ms/formrecognizer/invoicefields

              if ("Description".equals(key)) {
                if (DocumentFieldType.STRING == documentField.getType()) {
                  String name = documentField.getValueAsString();
                  System.out.printf("Description: %s, confidence: %.2fs%n",
                    name, documentField.getConfidence());
                }
              }
              if ("Quantity".equals(key)) {
                if (DocumentFieldType.DOUBLE == documentField.getType()) {
                  Double quantity = documentField.getValueAsDouble();
                  System.out.printf("Quantity: %f, confidence: %.2f%n",
                    quantity, documentField.getConfidence());
                }
              }
              if ("UnitPrice".equals(key)) {
                if (DocumentFieldType.DOUBLE == documentField.getType()) {
                  Double unitPrice = documentField.getValueAsDouble();
                  System.out.printf("Unit Price: %f, confidence: %.2f%n",
                    unitPrice, documentField.getConfidence());
                }
              }
              if ("ProductCode".equals(key)) {
                if (DocumentFieldType.DOUBLE == documentField.getType()) {
                  Double productCode = documentField.getValueAsDouble();
                  System.out.printf("Product Code: %f, confidence: %.2f%n",
                    productCode, documentField.getConfidence());
                }
              }
            }));
        }
      }
    }
  }
}

Skapa och köra programmet

När du har lagt till ett kodexempel i programmet går du tillbaka till huvudprojektkatalogen – doc-intel-app.

1. Skapa ditt program med build kommandot :

   gradle build
   

2. Kör programmet med run kommandot :

   gradle run
   

REST API-referenspaket för klientbibliotek | (npm) | Exempel |som stöds REST API-version |

Klientbibliotekets SDK-referens | REST API-referenspaket | (npm) | Exempel |som stöds REST API-version |

Klientbibliotekets SDK-referens | REST API-referenspaket | (npm) | Exempel |som stöds REST API-version |

4. ai-document-intelligence Installera klientbiblioteket och azure/identity npm-paketen:

   npm i @azure-rest/ai-document-intelligence@1.0.0-beta.3 @azure/core-auth
   

   Appens package.json fil uppdateras med beroendena.

4. ai-form-recognizer Installera klientbiblioteket och azure/identity npm-paketen:

   npm i @azure/ai-form-recognizer@5.0.0 @azure/identity
   

   • Appens package.json fil uppdateras med beroendena.

4. ai-form-recognizer Installera klientbiblioteket och azure/identity npm-paketen:

   npm i @azure/ai-form-recognizer@4.0.0 @azure/identity
   

Om du vill interagera med document intelligence-tjänsten måste du skapa en instans av DocumentIntelligenceClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentIntelligenceClient instans med och dokumentinformationen AzureKeyCredential endpoint.

Om du vill interagera med document intelligence-tjänsten måste du skapa en instans av DocumentAnalysisClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentAnalysisClient instans med AzureKeyCredential och din Formigenkänning endpoint.

    const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");

  const { AzureKeyCredential } = require("@azure/core-auth");

    // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
    const key = "<your-key>";
    const endpoint = "<your-endpoint>";

    // sample document
    const formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

   async function main() {
    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key));


    const initialResponse = await client
      .path("/documentModels/{modelId}:analyze", "prebuilt-layout")
      .post({
        contentType: "application/json",
        body: {
          urlSource: formUrl
        },
       });

       if (isUnexpected(initialResponse)) {
       throw initialResponse.body.error;
     }

    const poller = await getLongRunningPoller(client, initialResponse);
    const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;

    const documents = analyzeResult?.documents;

    const document = documents && documents[0];
    if (!document) {
    throw new Error("Expected at least one document in the result.");
    }

    console.log(
    "Extracted document:",
    document.docType,
    `(confidence: ${document.confidence || "<undefined>"})`,
    );
    console.log("Fields:", document.fields);
}

main().catch((error) => {
    console.error("An error occurred:", error);
    process.exit(1);
});

Kör ditt program

När du har lagt till ett kodexempel i programmet kör du programmet:

1. Gå till mappen där du har ditt dokumentinformationsprogram (doc-intel-app).

2. Skriv följande kommando i terminalen:

   node index.js
   

Lägg till följande kodexempel i index.js filen. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

 const { AzureKeyCredential, DocumentAnalysisClient } = require("@azure/ai-form-recognizer");

    // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
    const key = "<your-key>";
    const endpoint = "<your-endpoint>";

    // sample document
  const formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

  async function main() {
    const client = new DocumentAnalysisClient(endpoint, new AzureKeyCredential(key));

    const poller = await client.beginAnalyzeDocumentFromUrl("prebuilt-layout", formUrl);

    const {
        pages,
        tables
    } = await poller.pollUntilDone();

    if (pages.length <= 0) {
        console.log("No pages were extracted from the document.");
    } else {
        console.log("Pages:");
        for (const page of pages) {
            console.log("- Page", page.pageNumber, `(unit: ${page.unit})`);
            console.log(`  ${page.width}x${page.height}, angle: ${page.angle}`);
            console.log(`  ${page.lines.length} lines, ${page.words.length} words`);
        }
    }

    if (tables.length <= 0) {
        console.log("No tables were extracted from the document.");
    } else {
        console.log("Tables:");
        for (const table of tables) {
            console.log(
                `- Extracted table: ${table.columnCount} columns, ${table.rowCount} rows (${table.cells.length} cells)`
            );
        }
    }
}

main().catch((error) => {
    console.error("An error occurred:", error);
    process.exit(1);
});

Kör ditt program

När du har lagt till ett kodexempel i programmet kör du programmet:

1. Gå till mappen där du har ditt dokumentinformationsprogram (doc-intel-app).

2. Skriv följande kommando i terminalen:

   node index.js
   

Utdata för layoutmodell

Här är ett kodfragment av förväntade utdata:

Pages:
- Page 1 (unit: inch)
  8.5x11, angle: 0
  69 lines, 425 words
Tables:
- Extracted table: 3 columns, 5 rows (15 cells)

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa layoutmodellens utdata.

const DocumentIntelligence = require("@azure-rest/ai-document-intelligence").default,
  { getLongRunningPoller, isUnexpected } = require("@azure-rest/ai-document-intelligence");

const { AzureKeyCredential } = require("@azure/core-auth");

    // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
    const key = "<your-key>";
    const endpoint = "<your-endpoint>";

    // sample document
    const invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"

async function main() {

    const client = DocumentIntelligence(endpoint, new AzureKeyCredential(key));

    const initialResponse = await client
    .path("/documentModels/{modelId}:analyze", "prebuilt-invoice")
    .post({
      contentType: "application/json",
      body: {
        // The Document Intelligence service will access the URL to the invoice image and extract data from it
        urlSource: invoiceUrl,
      },
    });

    if (isUnexpected(initialResponse)) {
       throw initialResponse.body.error;
     }

    const poller = await getLongRunningPoller(client, initialResponse);

    poller.onProgress((state) => console.log("Operation:", state.result, state.status));
    const analyzeResult = (await poller.pollUntilDone()).body.analyzeResult;

    const documents = analyzeResult?.documents;

    const result = documents && documents[0];
    if (result) {
      console.log(result.fields);
    } else {
      throw new Error("Expected at least one invoice in the result.");
    }

console.log(
    "Extracted invoice:",
    document.docType,
    `(confidence: ${document.confidence || "<undefined>"})`,
  );
  console.log("Fields:", document.fields);
}


main().catch((error) => {
    console.error("An error occurred:", error);
    process.exit(1);
});

Kör ditt program

När du har lagt till ett kodexempel i programmet kör du programmet:

1. Gå till mappen där du har ditt dokumentinformationsprogram (doc-intel-app).

2. Skriv följande kommando i terminalen:

   node index.js
   

 const {
    AzureKeyCredential,
    DocumentAnalysisClient
} = require("@azure/ai-form-recognizer");

// set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
const key = "<your-key>";
const endpoint = "<your-endpoint>";
// sample document
invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"

async function main() {
    const client = new DocumentAnalysisClient(endpoint, new AzureKeyCredential(key));

    const poller = await client.beginAnalyzeDocumentFromUrl("prebuilt-invoice", invoiceUrl);

    const {
        pages,
        tables
    } = await poller.pollUntilDone();

    if (pages.length <= 0) {
        console.log("No pages were extracted from the document.");
    } else {
        console.log("Pages:");
        for (const page of pages) {
            console.log("- Page", page.pageNumber, `(unit: ${page.unit})`);
            console.log(`  ${page.width}x${page.height}, angle: ${page.angle}`);
            console.log(`  ${page.lines.length} lines, ${page.words.length} words`);

            if (page.lines && page.lines.length > 0) {
                console.log("  Lines:");

                for (const line of page.lines) {
                    console.log(`  - "${line.content}"`);

                    // The words of the line can also be iterated independently. The words are computed based on their
                    // corresponding spans.
                    for (const word of line.words()) {
                        console.log(`    - "${word.content}"`);
                    }
                }
            }
        }
    }

    if (tables.length <= 0) {
        console.log("No tables were extracted from the document.");
    } else {
        console.log("Tables:");
        for (const table of tables) {
            console.log(
                `- Extracted table: ${table.columnCount} columns, ${table.rowCount} rows (${table.cells.length} cells)`
            );
        }
    }
}

main().catch((error) => {
    console.error("An error occurred:", error);
    process.exit(1);
});

Kör ditt program

När du har lagt till ett kodexempel i programmet kör du programmet:

1. Gå till mappen där du har ditt dokumentinformationsprogram (doc-intel-app).

2. Skriv följande kommando i terminalen:

   node index.js
   

Fördefinierade modellutdata

Här är ett kodfragment av förväntade utdata:

  Vendor Name: CONTOSO LTD.
  Customer Name: MICROSOFT CORPORATION
  Invoice Date: 2019-11-15T00:00:00.000Z
  Due Date: 2019-12-15T00:00:00.000Z
  Items:
  - <no product code>
    Description: Test for 23 fields
    Quantity: 1
    Date: undefined
    Unit: undefined
    Unit Price: 1
    Tax: undefined
    Amount: 100

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa de fördefinierade fakturamodellutdata.

const { AzureKeyCredential, DocumentAnalysisClient } = require("@azure/ai-form-recognizer");

  // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
      const key = "<your-key>";
      const endpoint = "<your-endpoint>";
// sample document
    invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"

async function main() {
    const client = new DocumentAnalysisClient(endpoint, new AzureKeyCredential(key));

    const poller = await client.beginAnalyzeDocument("prebuilt-invoice", invoiceUrl);

    const {
    documents: [document],
  } = await poller.pollUntilDone();


  if (document) {
    const {
      vendorName,
      customerName,
      invoiceDate,
      dueDate,
      items,
      subTotal,
      previousUnpaidBalance,
      totalTax,
      amountDue,
    } = document.fields;

    // The invoice model has many fields. For details, *see* [Invoice model field extraction](../../prebuilt/invoice.md#field-extraction)
    console.log("Vendor Name:", vendorName && vendorName.value);
    console.log("Customer Name:", customerName && customerName.value);
    console.log("Invoice Date:", invoiceDate && invoiceDate.value);
    console.log("Due Date:", dueDate && dueDate.value);

    console.log("Items:");
    for (const item of (items && items.values) || []) {
      const { productCode, description, quantity, date, unit, unitPrice, tax, amount } =
        item.properties;

      console.log("-", (productCode && productCode.value) || "<no product code>");
      console.log("  Description:", description && description.value);
      console.log("  Quantity:", quantity && quantity.value);
      console.log("  Date:", date && date.value);
      console.log("  Unit:", unit && unit.value);
      console.log("  Unit Price:", unitPrice && unitPrice.value);
      console.log("  Tax:", tax && tax.value);
      console.log("  Amount:", amount && amount.value);
    }

    console.log("Subtotal:", subTotal && subTotal.value);
    console.log("Previous Unpaid Balance:", previousUnpaidBalance && previousUnpaidBalance.value);
    console.log("Tax:", totalTax && totalTax.value);
    console.log("Amount Due:", amountDue && amountDue.value);
  } else {
    throw new Error("Expected at least one receipt in the result.");
  }
}


main().catch((error) => {
    console.error("An error occurred:", error);
    process.exit(1);
});

Kör ditt program

När du har lagt till ett kodexempel i programmet kör du programmet:

1. Gå till mappen där du har ditt dokumentinformationsprogram (doc-intel-app).

2. Skriv följande kommando i terminalen:

   node index.js
   

Klientbibliotekets SDK-referens | REST API-referenspaket | (PyPi) | Exempel | som stöds REST API-version |

Klientbibliotekets SDK-referens | REST API-referenspaket | (PyPi) | Exempel | som stöds REST API-version |

Klientbibliotekets SDK-referens | REST API-referenspaket | (PyPi) | Exempel | som stöds REST API-version |

pip install azure-ai-documentintelligence==1.0.0b4

pip install azure-ai-formrecognizer==3.3.0

pip install azure-ai-formrecognizer==3.2.0b6

Om du vill interagera med document intelligence-tjänsten måste du skapa en instans av DocumentIntelligenceClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentIntelligenceClient instans med och dokumentinformationen AzureKeyCredential endpoint.

1. Skapa en ny Python-fil med namnet doc_intel_quickstart.py i önskad redigerare eller IDE.

2. Öppna filen doc_intel_quickstart.py och välj något av följande kodexempel för att kopiera och klistra in i ditt program:

   • Layout

   • Fördefinierad faktura

Om du vill interagera med document intelligence-tjänsten måste du skapa en instans av DocumentAnalysisClient klassen. För att göra det skapar du en AzureKeyCredential med din key från Azure Portal och en DocumentAnalysisClient instans med och dokumentinformationen AzureKeyCredential endpoint.

1. Skapa en ny Python-fil med namnet form_recognizer_quickstart.py i önskad redigerare eller IDE.

2. Öppna filen form_recognizer_quickstart.py och välj något av följande kodexempel för att kopiera och klistra in i ditt program:

   • Layout

   • Fördefinierad faktura

Lägg till följande kodexempel i ditt doc_intel_quickstart.py program. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
from azure.ai.documentintelligence.models import AnalyzeDocumentRequest

# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"

# helper functions

def get_words(page, line):
    result = []
    for word in page.words:
        if _in_span(word, line.spans):
            result.append(word)
    return result


def _in_span(word, spans):
    for span in spans:
        if word.span.offset >= span.offset and (
            word.span.offset + word.span.length
        ) <= (span.offset + span.length):
            return True
    return False


def analyze_layout():
    # sample document
    formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

    document_intelligence_client = DocumentIntelligenceClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_intelligence_client.begin_analyze_document(
        "prebuilt-layout", AnalyzeDocumentRequest(url_source=formUrl
    ))

    result: AnalyzeResult = poller.result()

    if result.styles and any([style.is_handwritten for style in result.styles]):
        print("Document contains handwritten content")
    else:
        print("Document does not contain handwritten content")

    for page in result.pages:
        print(f"----Analyzing layout from page #{page.page_number}----")
        print(
            f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}"
        )

        if page.lines:
            for line_idx, line in enumerate(page.lines):
                words = get_words(page, line)
                print(
                    f"...Line # {line_idx} has word count {len(words)} and text '{line.content}' "
                    f"within bounding polygon '{line.polygon}'"
                )

                for word in words:
                    print(
                        f"......Word '{word.content}' has a confidence of {word.confidence}"
                    )

        if page.selection_marks:
            for selection_mark in page.selection_marks:
                print(
                    f"Selection mark is '{selection_mark.state}' within bounding polygon "
                    f"'{selection_mark.polygon}' and has a confidence of {selection_mark.confidence}"
                )

    if result.tables:
        for table_idx, table in enumerate(result.tables):
            print(
                f"Table # {table_idx} has {table.row_count} rows and "
                f"{table.column_count} columns"
            )
            if table.bounding_regions:
                for region in table.bounding_regions:
                    print(
                        f"Table # {table_idx} location on page: {region.page_number} is {region.polygon}"
                    )
            for cell in table.cells:
                print(
                    f"...Cell[{cell.row_index}][{cell.column_index}] has text '{cell.content}'"
                )
                if cell.bounding_regions:
                    for region in cell.bounding_regions:
                        print(
                            f"...content on page {region.page_number} is within bounding polygon '{region.polygon}'"
                        )

    print("----------------------------------------")


if __name__ == "__main__":
    analyze_layout()

Kör programmet

När du har lagt till ett kodexempel i ditt program skapar och kör du programmet:

1. Gå till mappen där du har din doc_intel_quickstart.py-fil .

2. Skriv följande kommando i terminalen:

   python doc_intel_quickstart.py
   

Om du vill analysera en viss fil på en URL använder begin_analyze_document_from_url du metoden och skickar in prebuilt-layout som modell-ID. Det returnerade värdet är ett result objekt som innehåller data om det skickade dokumentet.

Lägg till följande kodexempel i ditt form_recognizer_quickstart.py-program. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

# import libraries
import os
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential

# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"

def format_polygon(polygon):
    if not polygon:
        return "N/A"
    return ", ".join(["[{}, {}]".format(p.x, p.y) for p in polygon])

def analyze_layout():
    # sample document
    formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

    document_analysis_client = DocumentAnalysisClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_analysis_client.begin_analyze_document_from_url(
            "prebuilt-layout", formUrl)
    result = poller.result()

    for idx, style in enumerate(result.styles):
        print(
            "Document contains {} content".format(
                "handwritten" if style.is_handwritten else "no handwritten"
            )
        )

    for page in result.pages:
        print("----Analyzing layout from page #{}----".format(page.page_number))
        print(
            "Page has width: {} and height: {}, measured with unit: {}".format(
                page.width, page.height, page.unit
            )
        )

        for line_idx, line in enumerate(page.lines):
            words = line.get_words()
            print(
                "...Line # {} has word count {} and text '{}' within bounding box '{}'".format(
                    line_idx,
                    len(words),
                    line.content,
                    format_polygon(line.polygon),
                )
            )

            for word in words:
                print(
                    "......Word '{}' has a confidence of {}".format(
                        word.content, word.confidence
                    )
                )

        for selection_mark in page.selection_marks:
            print(
                "...Selection mark is '{}' within bounding box '{}' and has a confidence of {}".format(
                    selection_mark.state,
                    format_polygon(selection_mark.polygon),
                    selection_mark.confidence,
                )
            )

    for table_idx, table in enumerate(result.tables):
        print(
            "Table # {} has {} rows and {} columns".format(
                table_idx, table.row_count, table.column_count
            )
        )
        for region in table.bounding_regions:
            print(
                "Table # {} location on page: {} is {}".format(
                    table_idx,
                    region.page_number,
                    format_polygon(region.polygon),
                )
            )
        for cell in table.cells:
            print(
                "...Cell[{}][{}] has content '{}'".format(
                    cell.row_index,
                    cell.column_index,
                    cell.content,
                )
            )
            for region in cell.bounding_regions:
                print(
                    "...content on page {} is within bounding box '{}'".format(
                        region.page_number,
                        format_polygon(region.polygon),
                    )
                )

    print("----------------------------------------")


if __name__ == "__main__":
    analyze_layout()

Kör programmet

När du har lagt till ett kodexempel i ditt program skapar och kör du programmet:

1. Gå till den mapp där du har din form_recognizer_quickstart.py-fil .

2. Skriv följande kommando i terminalen:

   python form_recognizer_quickstart.py
   

Utdata för layoutmodell

Här är ett kodfragment av förväntade utdata:

  ----Analyzing layout from page #1----
  Page has width: 8.5 and height: 11.0, measured with unit: inch
  ...Line # 0 has word count 2 and text 'UNITED STATES' within bounding box '[3.4915, 0.6828], [5.0116, 0.6828], [5.0116, 0.8265], [3.4915, 0.8265]'
  ......Word 'UNITED' has a confidence of 1.0
  ......Word 'STATES' has a confidence of 1.0
  ...Line # 1 has word count 4 and text 'SECURITIES AND EXCHANGE COMMISSION' within bounding box '[2.1937, 0.9061], [6.297, 0.9061], [6.297, 1.0498], [2.1937, 1.0498]'
  ......Word 'SECURITIES' has a confidence of 1.0
  ......Word 'AND' has a confidence of 1.0
  ......Word 'EXCHANGE' has a confidence of 1.0
  ......Word 'COMMISSION' has a confidence of 1.0
  ...Line # 2 has word count 3 and text 'Washington, D.C. 20549' within bounding box '[3.4629, 1.1179], [5.031, 1.1179], [5.031, 1.2483], [3.4629, 1.2483]'
  ......Word 'Washington,' has a confidence of 1.0
  ......Word 'D.C.' has a confidence of 1.0

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa layoutmodellens utdata.

Lägg till följande kodexempel i ditt form_recognizer_quickstart.py-program. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

# import libraries
import os
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential

# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"


def analyze_layout():
    # sample document
    formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

    document_analysis_client = DocumentAnalysisClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_analysis_client.begin_analyze_document_from_url(
        "prebuilt-layout", formUrl
    )
    result = poller.result()

    for idx, style in enumerate(result.styles):
        print(
            "Document contains {} content".format(
                "handwritten" if style.is_handwritten else "no handwritten"
            )
        )

    for page in result.pages:
        print("----Analyzing layout from page #{}----".format(page.page_number))
        print(
            "Page has width: {} and height: {}, measured with unit: {}".format(
                page.width, page.height, page.unit
            )
        )

        for line_idx, line in enumerate(page.lines):
            words = line.get_words()
            print(
                "...Line # {} has word count {} and text '{}' within bounding polygon '{}'".format(
                    line_idx,
                    len(words),
                    line.content,
                    format_polygon(line.polygon),
                )
            )

            for word in words:
                print(
                    "......Word '{}' has a confidence of {}".format(
                        word.content, word.confidence
                    )
                )

        for selection_mark in page.selection_marks:
            print(
                "...Selection mark is '{}' within bounding polygon '{}' and has a confidence of {}".format(
                    selection_mark.state,
                    format_polygon(selection_mark.polygon),
                    selection_mark.confidence,
                )
            )

    for table_idx, table in enumerate(result.tables):
        print(
            "Table # {} has {} rows and {} columns".format(
                table_idx, table.row_count, table.column_count
            )
        )
        for region in table.bounding_regions:
            print(
                "Table # {} location on page: {} is {}".format(
                    table_idx,
                    region.page_number,
                    format_polygon(region.polygon),
                )
            )
        for cell in table.cells:
            print(
                "...Cell[{}][{}] has content '{}'".format(
                    cell.row_index,
                    cell.column_index,
                    cell.content,
                )
            )
            for region in cell.bounding_regions:
                print(
                    "...content on page {} is within bounding polygon '{}'".format(
                        region.page_number,
                        format_polygon(region.polygon),
                    )
                )

    print("----------------------------------------")


if __name__ == "__main__":
    analyze_layout()

Kör programmet

När du har lagt till ett kodexempel i ditt program skapar och kör du programmet:

1. Gå till den mapp där du har din form_recognizer_quickstart.py-fil .

2. Skriv följande kommando i terminalen:

   python form_recognizer_quickstart.py
   

Lägg till följande kodexempel i ditt doc_intel_quickstart.py program. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Document Intelligence-instans:

# import libraries
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.ai.documentintelligence.models import AnalyzeResult
from azure.ai.documentintelligence.models import AnalyzeDocumentRequest



# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"

def analyze_invoice():
    # sample document

    invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"

    document_intelligence_client = DocumentIntelligenceClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_intelligence_client.begin_analyze_document(
        "prebuilt-invoice", AnalyzeDocumentRequest(url_source=invoiceUrl)
    )
    invoices = poller.result()

    if invoices.documents:
        for idx, invoice in enumerate(invoices.documents):
            print(f"--------Analyzing invoice #{idx + 1}--------")
            vendor_name = invoice.fields.get("VendorName")
            if vendor_name:
                print(
                    f"Vendor Name: {vendor_name.get('content')} has confidence: {vendor_name.get('confidence')}"
                )
            vendor_address = invoice.fields.get("VendorAddress")
            if vendor_address:
                print(
                    f"Vendor Address: {vendor_address.get('content')} has confidence: {vendor_address.get('confidence')}"
                )
            vendor_address_recipient = invoice.fields.get("VendorAddressRecipient")
            if vendor_address_recipient:
                print(
                    f"Vendor Address Recipient: {vendor_address_recipient.get('content')} has confidence: {vendor_address_recipient.get('confidence')}"
                )
            customer_name = invoice.fields.get("CustomerName")
            if customer_name:
                print(
                    f"Customer Name: {customer_name.get('content')} has confidence: {customer_name.get('confidence')}"
                )
            customer_id = invoice.fields.get("CustomerId")
            if customer_id:
                print(
                    f"Customer Id: {customer_id.get('content')} has confidence: {customer_id.get('confidence')}"
                )
            customer_address = invoice.fields.get("CustomerAddress")
            if customer_address:
                print(
                    f"Customer Address: {customer_address.get('content')} has confidence: {customer_address.get('confidence')}"
                )
            customer_address_recipient = invoice.fields.get("CustomerAddressRecipient")
            if customer_address_recipient:
                print(
                    f"Customer Address Recipient: {customer_address_recipient.get('content')} has confidence: {customer_address_recipient.get('confidence')}"
                )
            invoice_id = invoice.fields.get("InvoiceId")
            if invoice_id:
                print(
                    f"Invoice Id: {invoice_id.get('content')} has confidence: {invoice_id.get('confidence')}"
                )
            invoice_date = invoice.fields.get("InvoiceDate")
            if invoice_date:
                print(
                    f"Invoice Date: {invoice_date.get('content')} has confidence: {invoice_date.get('confidence')}"
                )
            invoice_total = invoice.fields.get("InvoiceTotal")
            if invoice_total:
                print(
                    f"Invoice Total: {invoice_total.get('content')} has confidence: {invoice_total.get('confidence')}"
                )
            due_date = invoice.fields.get("DueDate")
            if due_date:
                print(
                    f"Due Date: {due_date.get('content')} has confidence: {due_date.get('confidence')}"
                )
            purchase_order = invoice.fields.get("PurchaseOrder")
            if purchase_order:
                print(
                    f"Purchase Order: {purchase_order.get('content')} has confidence: {purchase_order.get('confidence')}"
                )
            billing_address = invoice.fields.get("BillingAddress")
            if billing_address:
                print(
                    f"Billing Address: {billing_address.get('content')} has confidence: {billing_address.get('confidence')}"
                )
            billing_address_recipient = invoice.fields.get("BillingAddressRecipient")
            if billing_address_recipient:
                print(
                    f"Billing Address Recipient: {billing_address_recipient.get('content')} has confidence: {billing_address_recipient.get('confidence')}"
                )
            shipping_address = invoice.fields.get("ShippingAddress")
            if shipping_address:
                print(
                    f"Shipping Address: {shipping_address.get('content')} has confidence: {shipping_address.get('confidence')}"
                )
            shipping_address_recipient = invoice.fields.get("ShippingAddressRecipient")
            if shipping_address_recipient:
                print(
                    f"Shipping Address Recipient: {shipping_address_recipient.get('content')} has confidence: {shipping_address_recipient.get('confidence')}"
                )
            print("Invoice items:")
            for idx, item in enumerate(invoice.fields.get("Items").get("valueArray")):
                print(f"...Item #{idx + 1}")
                item_description = item.get("valueObject").get("Description")
                if item_description:
                    print(
                        f"......Description: {item_description.get('content')} has confidence: {item_description.get('confidence')}"
                    )
                item_quantity = item.get("valueObject").get("Quantity")
                if item_quantity:
                    print(
                        f"......Quantity: {item_quantity.get('content')} has confidence: {item_quantity.get('confidence')}"
                    )
                unit = item.get("valueObject").get("Unit")
                if unit:
                    print(
                        f"......Unit: {unit.get('content')} has confidence: {unit.get('confidence')}"
                    )
                unit_price = item.get("valueObject").get("UnitPrice")
                if unit_price:
                    unit_price_code = (
                        unit_price.get("valueCurrency").get("currencyCode")
                        if unit_price.get("valueCurrency").get("currencyCode")
                        else ""
                    )
                    print(
                        f"......Unit Price: {unit_price.get('content')}{unit_price_code} has confidence: {unit_price.get('confidence')}"
                    )
                product_code = item.get("valueObject").get("ProductCode")
                if product_code:
                    print(
                        f"......Product Code: {product_code.get('content')} has confidence: {product_code.get('confidence')}"
                    )
                item_date = item.get("valueObject").get("Date")
                if item_date:
                    print(
                        f"......Date: {item_date.get('content')} has confidence: {item_date.get('confidence')}"
                    )
                tax = item.get("valueObject").get("Tax")
                if tax:
                    print(
                        f"......Tax: {tax.get('content')} has confidence: {tax.get('confidence')}"
                    )
                amount = item.get("valueObject").get("Amount")
                if amount:
                    print(
                        f"......Amount: {amount.get('content')} has confidence: {amount.get('confidence')}"
                    )
            subtotal = invoice.fields.get("SubTotal")
            if subtotal:
                print(
                    f"Subtotal: {subtotal.get('content')} has confidence: {subtotal.get('confidence')}"
                )
            total_tax = invoice.fields.get("TotalTax")
            if total_tax:
                print(
                    f"Total Tax: {total_tax.get('content')} has confidence: {total_tax.get('confidence')}"
                )
            previous_unpaid_balance = invoice.fields.get("PreviousUnpaidBalance")
            if previous_unpaid_balance:
                print(
                    f"Previous Unpaid Balance: {previous_unpaid_balance.get('content')} has confidence: {previous_unpaid_balance.get('confidence')}"
                )
            amount_due = invoice.fields.get("AmountDue")
            if amount_due:
                print(
                    f"Amount Due: {amount_due.get('content')} has confidence: {amount_due.get('confidence')}"
                )
            service_start_date = invoice.fields.get("ServiceStartDate")
            if service_start_date:
                print(
                    f"Service Start Date: {service_start_date.get('content')} has confidence: {service_start_date.get('confidence')}"
                )
            service_end_date = invoice.fields.get("ServiceEndDate")
            if service_end_date:
                print(
                    f"Service End Date: {service_end_date.get('content')} has confidence: {service_end_date.get('confidence')}"
                )
            service_address = invoice.fields.get("ServiceAddress")
            if service_address:
                print(
                    f"Service Address: {service_address.get('content')} has confidence: {service_address.get('confidence')}"
                )
            service_address_recipient = invoice.fields.get("ServiceAddressRecipient")
            if service_address_recipient:
                print(
                    f"Service Address Recipient: {service_address_recipient.get('content')} has confidence: {service_address_recipient.get('confidence')}"
                )
            remittance_address = invoice.fields.get("RemittanceAddress")
            if remittance_address:
                print(
                    f"Remittance Address: {remittance_address.get('content')} has confidence: {remittance_address.get('confidence')}"
                )
            remittance_address_recipient = invoice.fields.get(
                "RemittanceAddressRecipient"
            )
            if remittance_address_recipient:
                print(
                    f"Remittance Address Recipient: {remittance_address_recipient.get('content')} has confidence: {remittance_address_recipient.get('confidence')}"
                )


          print("----------------------------------------")


if __name__ == "__main__":
    analyze_invoice()

Kör programmet

När du har lagt till ett kodexempel i ditt program skapar och kör du programmet:

1. Gå till mappen där du har din doc_intel_quickstart.py-fil .

2. Skriv följande kommando i terminalen:

   python doc_intel_quickstart.py
   

Om du vill analysera en viss fil vid en URI använder begin_analyze_document_from_url du metoden och skickar prebuilt-invoice den som modell-ID. Det returnerade värdet är ett result objekt som innehåller data om det skickade dokumentet.

Lägg till följande kodexempel i ditt form_recognizer_quickstart.py-program. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

# import libraries
import os
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential

# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"


def format_bounding_region(bounding_regions):
    if not bounding_regions:
        return "N/A"
    return ", ".join(
        "Page #{}: {}".format(region.page_number, format_polygon(region.polygon))
        for region in bounding_regions
    )


def format_polygon(polygon):
    if not polygon:
        return "N/A"
    return ", ".join(["[{}, {}]".format(p.x, p.y) for p in polygon])


def analyze_invoice():

    invoiceUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf"

    document_analysis_client = DocumentAnalysisClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_analysis_client.begin_analyze_document_from_url(
        "prebuilt-invoice", invoiceUrl
    )
    invoices = poller.result()

    for idx, invoice in enumerate(invoices.documents):
        print("--------Recognizing invoice #{}--------".format(idx + 1))
        vendor_name = invoice.fields.get("VendorName")
        if vendor_name:
            print(
                "Vendor Name: {} has confidence: {}".format(
                    vendor_name.value, vendor_name.confidence
                )
            )
        vendor_address = invoice.fields.get("VendorAddress")
        if vendor_address:
            print(
                "Vendor Address: {} has confidence: {}".format(
                    vendor_address.value, vendor_address.confidence
                )
            )
        vendor_address_recipient = invoice.fields.get("VendorAddressRecipient")
        if vendor_address_recipient:
            print(
                "Vendor Address Recipient: {} has confidence: {}".format(
                    vendor_address_recipient.value, vendor_address_recipient.confidence
                )
            )
        customer_name = invoice.fields.get("CustomerName")
        if customer_name:
            print(
                "Customer Name: {} has confidence: {}".format(
                    customer_name.value, customer_name.confidence
                )
            )
        customer_id = invoice.fields.get("CustomerId")
        if customer_id:
            print(
                "Customer Id: {} has confidence: {}".format(
                    customer_id.value, customer_id.confidence
                )
            )
        customer_address = invoice.fields.get("CustomerAddress")
        if customer_address:
            print(
                "Customer Address: {} has confidence: {}".format(
                    customer_address.value, customer_address.confidence
                )
            )
        customer_address_recipient = invoice.fields.get("CustomerAddressRecipient")
        if customer_address_recipient:
            print(
                "Customer Address Recipient: {} has confidence: {}".format(
                    customer_address_recipient.value,
                    customer_address_recipient.confidence,
                )
            )
        invoice_id = invoice.fields.get("InvoiceId")
        if invoice_id:
            print(
                "Invoice Id: {} has confidence: {}".format(
                    invoice_id.value, invoice_id.confidence
                )
            )
        invoice_date = invoice.fields.get("InvoiceDate")
        if invoice_date:
            print(
                "Invoice Date: {} has confidence: {}".format(
                    invoice_date.value, invoice_date.confidence
                )
            )
        invoice_total = invoice.fields.get("InvoiceTotal")
        if invoice_total:
            print(
                "Invoice Total: {} has confidence: {}".format(
                    invoice_total.value, invoice_total.confidence
                )
            )
        due_date = invoice.fields.get("DueDate")
        if due_date:
            print(
                "Due Date: {} has confidence: {}".format(
                    due_date.value, due_date.confidence
                )
            )
        purchase_order = invoice.fields.get("PurchaseOrder")
        if purchase_order:
            print(
                "Purchase Order: {} has confidence: {}".format(
                    purchase_order.value, purchase_order.confidence
                )
            )
        billing_address = invoice.fields.get("BillingAddress")
        if billing_address:
            print(
                "Billing Address: {} has confidence: {}".format(
                    billing_address.value, billing_address.confidence
                )
            )
        billing_address_recipient = invoice.fields.get("BillingAddressRecipient")
        if billing_address_recipient:
            print(
                "Billing Address Recipient: {} has confidence: {}".format(
                    billing_address_recipient.value,
                    billing_address_recipient.confidence,
                )
            )
        shipping_address = invoice.fields.get("ShippingAddress")
        if shipping_address:
            print(
                "Shipping Address: {} has confidence: {}".format(
                    shipping_address.value, shipping_address.confidence
                )
            )
        shipping_address_recipient = invoice.fields.get("ShippingAddressRecipient")
        if shipping_address_recipient:
            print(
                "Shipping Address Recipient: {} has confidence: {}".format(
                    shipping_address_recipient.value,
                    shipping_address_recipient.confidence,
                )
            )
        print("Invoice items:")
        for idx, item in enumerate(invoice.fields.get("Items").value):
            print("...Item #{}".format(idx + 1))
            item_description = item.value.get("Description")
            if item_description:
                print(
                    "......Description: {} has confidence: {}".format(
                        item_description.value, item_description.confidence
                    )
                )
            item_quantity = item.value.get("Quantity")
            if item_quantity:
                print(
                    "......Quantity: {} has confidence: {}".format(
                        item_quantity.value, item_quantity.confidence
                    )
                )
            unit = item.value.get("Unit")
            if unit:
                print(
                    "......Unit: {} has confidence: {}".format(
                        unit.value, unit.confidence
                    )
                )
            unit_price = item.value.get("UnitPrice")
            if unit_price:
                print(
                    "......Unit Price: {} has confidence: {}".format(
                        unit_price.value, unit_price.confidence
                    )
                )
            product_code = item.value.get("ProductCode")
            if product_code:
                print(
                    "......Product Code: {} has confidence: {}".format(
                        product_code.value, product_code.confidence
                    )
                )
            item_date = item.value.get("Date")
            if item_date:
                print(
                    "......Date: {} has confidence: {}".format(
                        item_date.value, item_date.confidence
                    )
                )
            tax = item.value.get("Tax")
            if tax:
                print(
                    "......Tax: {} has confidence: {}".format(tax.value, tax.confidence)
                )
            amount = item.value.get("Amount")
            if amount:
                print(
                    "......Amount: {} has confidence: {}".format(
                        amount.value, amount.confidence
                    )
                )
        subtotal = invoice.fields.get("SubTotal")
        if subtotal:
            print(
                "Subtotal: {} has confidence: {}".format(
                    subtotal.value, subtotal.confidence
                )
            )
        total_tax = invoice.fields.get("TotalTax")
        if total_tax:
            print(
                "Total Tax: {} has confidence: {}".format(
                    total_tax.value, total_tax.confidence
                )
            )
        previous_unpaid_balance = invoice.fields.get("PreviousUnpaidBalance")
        if previous_unpaid_balance:
            print(
                "Previous Unpaid Balance: {} has confidence: {}".format(
                    previous_unpaid_balance.value, previous_unpaid_balance.confidence
                )
            )
        amount_due = invoice.fields.get("AmountDue")
        if amount_due:
            print(
                "Amount Due: {} has confidence: {}".format(
                    amount_due.value, amount_due.confidence
                )
            )
        service_start_date = invoice.fields.get("ServiceStartDate")
        if service_start_date:
            print(
                "Service Start Date: {} has confidence: {}".format(
                    service_start_date.value, service_start_date.confidence
                )
            )
        service_end_date = invoice.fields.get("ServiceEndDate")
        if service_end_date:
            print(
                "Service End Date: {} has confidence: {}".format(
                    service_end_date.value, service_end_date.confidence
                )
            )
        service_address = invoice.fields.get("ServiceAddress")
        if service_address:
            print(
                "Service Address: {} has confidence: {}".format(
                    service_address.value, service_address.confidence
                )
            )
        service_address_recipient = invoice.fields.get("ServiceAddressRecipient")
        if service_address_recipient:
            print(
                "Service Address Recipient: {} has confidence: {}".format(
                    service_address_recipient.value,
                    service_address_recipient.confidence,
                )
            )
        remittance_address = invoice.fields.get("RemittanceAddress")
        if remittance_address:
            print(
                "Remittance Address: {} has confidence: {}".format(
                    remittance_address.value, remittance_address.confidence
                )
            )
        remittance_address_recipient = invoice.fields.get("RemittanceAddressRecipient")
        if remittance_address_recipient:
            print(
                "Remittance Address Recipient: {} has confidence: {}".format(
                    remittance_address_recipient.value,
                    remittance_address_recipient.confidence,
                )
            )

        print("----------------------------------------")

if __name__ == "__main__":
    analyze_invoice()

Kör programmet

När du har lagt till ett kodexempel i ditt program skapar och kör du programmet:

1. Gå till den mapp där du har din form_recognizer_quickstart.py-fil .

2. Skriv följande kommando i terminalen:

   python form_recognizer_quickstart.py
   

Fördefinierade modellutdata

Här är ett kodfragment av förväntade utdata:

  --------Recognizing invoice #1--------
  Vendor Name: CONTOSO LTD. has confidence: 0.919
  Vendor Address: 123 456th St New York, NY, 10001 has confidence: 0.907
  Vendor Address Recipient: Contoso Headquarters has confidence: 0.919
  Customer Name: MICROSOFT CORPORATION has confidence: 0.84
  Customer Id: CID-12345 has confidence: 0.956
  Customer Address: 123 Other St, Redmond WA, 98052 has confidence: 0.909
  Customer Address Recipient: Microsoft Corp has confidence: 0.917
  Invoice Id: INV-100 has confidence: 0.972
  Invoice Date: 2019-11-15 has confidence: 0.971
  Invoice Total: CurrencyValue(amount=110.0, symbol=$) has confidence: 0.97
  Due Date: 2019-12-15 has confidence: 0.973

Om du vill visa hela utdata går du till Azure-exempellagringsplatsen på GitHub för att visa de fördefinierade fakturamodellutdata.

Lägg till följande kodexempel i ditt form_recognizer_quickstart.py-program. Se till att du uppdaterar nyckel- och slutpunktsvariablerna med värden från din Azure Portal Formigenkänning-instans:

# import libraries
import os
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential

# set `<your-endpoint>` and `<your-key>` variables with the values from the Azure portal
endpoint = "<your-endpoint>"
key = "<your-key>"


def format_polygon(polygon):
    if not polygon:
        return "N/A"
    return ", ".join(["[{}, {}]".format(p.x, p.y) for p in polygon])


def analyze_layout():
    # sample document
    formUrl = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-layout.pdf"

    document_analysis_client = DocumentAnalysisClient(
        endpoint=endpoint, credential=AzureKeyCredential(key)
    )

    poller = document_analysis_client.begin_analyze_document_from_url(
        "prebuilt-layout", formUrl
    )
    result = poller.result()

    for idx, style in enumerate(result.styles):
        print(
            "Document contains {} content".format(
                "handwritten" if style.is_handwritten else "no handwritten"
            )
        )


for page in result.pages:
    print("----Analyzing layout from page #{}----".format(page.page_number))
    print(
        "Page has width: {} and height: {}, measured with unit: {}".format(
            page.width, page.height, page.unit
        )
    )

    for line_idx, line in enumerate(page.lines):
        words = line.get_words()
        print(
            "...Line # {} has word count {} and text '{}' within bounding polygon '{}'".format(
                line_idx,
                len(words),
                line.content,
                format_polygon(line.polygon),
            )
        )

        for word in words:
            print(
                "......Word '{}' has a confidence of {}".format(
                    word.content, word.confidence
                )
            )

    for selection_mark in page.selection_marks:
        print(
            "...Selection mark is '{}' within bounding polygon '{}' and has a confidence of {}".format(
                selection_mark.state,
                format_polygon(selection_mark.polygon),
                selection_mark.confidence,
            )
        )

for table_idx, table in enumerate(result.tables):
    print(
        "Table # {} has {} rows and {} columns".format(
            table_idx, table.row_count, table.column_count
        )
    )
    for region in table.bounding_regions:
        print(
            "Table # {} location on page: {} is {}".format(
                table_idx,
                region.page_number,
                format_polygon(region.polygon),
            )
        )
    for cell in table.cells:
        print(
            "...Cell[{}][{}] has content '{}'".format(
                cell.row_index,
                cell.column_index,
                cell.content,
            )
        )
        for region in cell.bounding_regions:
            print(
                "...content on page {} is within bounding polygon '{}'".format(
                    region.page_number,
                    format_polygon(region.polygon),
                )
            )

print("----------------------------------------")


if __name__ == "__main__":
    analyze_layout()

Kör programmet

När du har lagt till ett kodexempel i ditt program skapar och kör du programmet:

1. Gå till den mapp där du har din form_recognizer_quickstart.py-fil .

2. Skriv följande kommando i terminalen:

   python form_recognizer_quickstart.py
   

| Rest API för | dokumentinformation som stöds av Azure SDKS

| Rest API för | dokumentinformation som stöds av Azure SDKS |

| Rest API för | dokumentinformation som stöds av Azure SDKS |

Exempeldokument

Exempeldokument

curl -v -i POST "{endpoint}/documentintelligence/documentModels/{modelId}:analyze?api-version=2024-07-31-preview" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {key}" --data-ascii "{'urlSource': '{your-document-url}'}"

curl -v -i POST "{endpoint}/formrecognizer/documentModels/{modelID}:analyze?api-version=2023-07-31" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {key}" --data-ascii "{'urlSource': '{your-document-url}'}"

curl -v -i POST "{endpoint}/formrecognizer/documentModels/{modelId}:analyze?api-version=2022-08-31" -H "Content-Type: application/json" -H "Ocp-Apim-Subscription-Key: {key}" --data-ascii "{'urlSource': '{your-document-url}'}"

När du har anropat API:et Analyze document anropar du API:et Hämta analysresultat för att hämta status för åtgärden och extraherade data. Innan du kör kommandot gör du följande ändringar:

När du har anropat API:et Analyze document anropar du API:et Hämta analysresultat för att hämta status för åtgärden och extraherade data. Innan du kör kommandot gör du följande ändringar:

När du har anropat API:et Analyze document anropar du API:et Hämta analysresultat för att hämta status för åtgärden och extraherade data. Innan du kör kommandot gör du följande ändringar:

curl -v -X GET "{endpoint}/documentintelligence/documentModels/{modelId}/analyzeResults/{resultId}?api-version=2024-07-31-preview" -H "Ocp-Apim-Subscription-Key: {key}"

curl -v -X GET "{endpoint}/formrecognizer/documentModels/{modelId}/analyzeResults/{resultId}?api-version=2023-07-31" -H "Ocp-Apim-Subscription-Key: {key}"

curl -v -X GET "{endpoint}/formrecognizer/documentModels/{modelId}/analyzeResults/{resultId}?api-version=2022-08-31" -H "Ocp-Apim-Subscription-Key: {key}"

{
    "status": "succeeded",
    "createdDateTime": "2024-03-25T19:31:37Z",
    "lastUpdatedDateTime": "2024-03-25T19:31:43Z",
    "analyzeResult": {
        "apiVersion": "2024-07-31-preview",
        "modelId": "prebuilt-invoice",
        "stringIndexType": "textElements"...
    ..."pages": [
            {
                "pageNumber": 1,
                "angle": 0,
                "width": 8.5,
                "height": 11,
                "unit": "inch",
                "words": [
                    {
                        "content": "CONTOSO",
                        "boundingBox": [
                            0.5911,
                            0.6857,
                            1.7451,
                            0.6857,
                            1.7451,
                            0.8664,
                            0.5911,
                            0.8664
                        ],
                        "confidence": 1,
                        "span": {
                            "offset": 0,
                            "length": 7
                                }
                      }],
              }]
      }
}

{
    "status": "succeeded",
    "createdDateTime": "2023-08-25T19:31:37Z",
    "lastUpdatedDateTime": "2023-08-25T19:31:43Z",
    "analyzeResult": {
        "apiVersion": "2023-07-31",
        "modelId": "prebuilt-invoice",
        "stringIndexType": "textElements"...
    ..."pages": [
            {
                "pageNumber": 1,
                "angle": 0,
                "width": 8.5,
                "height": 11,
                "unit": "inch",
                "words": [
                    {
                        "content": "CONTOSO",
                        "boundingBox": [
                            0.5911,
                            0.6857,
                            1.7451,
                            0.6857,
                            1.7451,
                            0.8664,
                            0.5911,
                            0.8664
                        ],
                        "confidence": 1,
                        "span": {
                            "offset": 0,
                            "length": 7
                                }
                      }],
              }]
      }
}

{
    "status": "succeeded",
    "createdDateTime": "2022-09-25T19:31:37Z",
    "lastUpdatedDateTime": "2022-09-25T19:31:43Z",
    "analyzeResult": {
        "apiVersion": "2022-08-31",
        "modelId": "prebuilt-invoice",
        "stringIndexType": "textElements"...
    ..."pages": [
            {
                "pageNumber": 1,
                "angle": 0,
                "width": 8.5,
                "height": 11,
                "unit": "inch",
                "words": [
                    {
                        "content": "CONTOSO",
                        "boundingBox": [
                            0.5911,
                            0.6857,
                            1.7451,
                            0.6857,
                            1.7451,
                            0.8664,
                            0.5911,
                            0.8664
                        ],
                        "confidence": 1,
                        "span": {
                            "offset": 0,
                            "length": 7
                                }
                      }],
              }]
      }
}