Azure DocumentIntelligence client library for Java - version 1.0.0-beta.4

Azure Document Intelligence (previously known as Form Recognizer) is a cloud service that uses machine learning to analyze text and structured data from your documents. It includes the following main features:

• Layout - Analyze text, table structures, and selection marks, along with their bounding region coordinates, from documents.
• Prebuilt - Analyze data from certain types of common documents (such as receipts, invoices, identity documents or US W2 tax forms) using prebuilt models.
• Custom - Build custom models to extract text, field values, selection marks, and table data from documents. Custom models are built with your own data, so they're tailored to your documents.
• Read - Read information about textual elements, such as page words and lines in addition to text language information.
• Classifiers - Build custom classifiers to categorize documents into predefined classes.

Source code | Package (Maven) | API reference documentation | Product Documentation | Samples

Getting started

Prerequisites

• Java Development Kit (JDK) with version 8 or above
  • Here are details about Java 8 client compatibility with Azure Certificate Authority.
• Azure Subscription
• AI Services or Document Intelligence account to use this package.

Adding the package to your product

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-documentintelligence</artifactId>
    <version>1.0.0-beta.4</version>
</dependency>

Note: This version of the client library defaults to the "2024-07-31-preview" version of the service.

This table shows the relationship between SDK versions and supported API versions of the service:

Note: Please rely on the older azure-ai-formrecognizer library through the older service API versions for retired models, such as "prebuilt-businessCard" and "prebuilt-document". For more information, see Changelog. The below table describes the relationship of each client and its supported API version(s):

Please see the Migration Guide for more information about migrating from azure-ai-formrecognizer to azure-ai-documentintelligence.

Authentication

In order to interact with the Azure Document Intelligence Service you'll need to create an instance of client class, DocumentIntelligenceAsyncClient or DocumentIntelligenceClient by using DocumentIntelligenceClientBuilder. To configure a client for use with Azure DocumentIntelligence, provide a valid endpoint URI to an Azure DocumentIntelligence resource along with a corresponding key credential, token credential, or Azure Identity credential that's authorized to use the Azure DocumentIntelligence resource.

Create an Azure DocumentIntelligence client with key credential

Get Azure DocumentIntelligence key credential from the Azure Portal.

DocumentIntelligenceClient documentIntelligenceClient = new DocumentIntelligenceClientBuilder()
    .credential(new AzureKeyCredential("{key}"))
    .endpoint("{endpoint}")
    .buildClient();

or

DocumentIntelligenceAdministrationClient client =
    new DocumentIntelligenceAdministrationClientBuilder()
        .credential(new AzureKeyCredential("{key}"))
        .endpoint("{endpoint}")
        .buildClient();

Create an Azure DocumentIntelligence client with Azure Active Directory credential

Azure SDK for Java supports an Azure Identity package, making it easy to get credentials from Microsoft identity platform.

Authentication with AAD requires some initial setup:

• Add the Azure Identity package

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-identity</artifactId>
    <version>1.13.2</version>
</dependency>

After setup, you can choose which type of credential from azure-identity to use. As an example, DefaultAzureCredential can be used to authenticate the client: Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

Authorization is easiest using DefaultAzureCredential. It finds the best credential to use in its running environment. For more information about using Azure Active Directory authorization with DocumentIntelligence service, please refer to the associated documentation.

DocumentIntelligenceAsyncClient documentIntelligenceAsyncClient = new DocumentIntelligenceClientBuilder()
    .credential(new DefaultAzureCredentialBuilder().build())
    .endpoint("{endpoint}")
    .buildAsyncClient();

Key concepts

DocumentAnalysisClient

The DocumentAnalysisClient and DocumentAnalysisAsyncClient provide both synchronous and asynchronous operations for analyzing input documents using custom and prebuilt models through the beginAnalyzeDocument API. See a full list of supported models here.

Sample code snippets to illustrate using a DocumentAnalysisClient here. More information about analyzing documents, including supported features, locales, and document types can be found here.

DocumentModelAdministrationClient

The DocumentModelAdministrationClient and DocumentModelAdministrationAsyncClient provide both synchronous and asynchronous operations

• Build custom document analysis models to analyze text content, fields, and values found in your custom documents. See example Build a document model. A DocumentModelDetails is returned indicating the document types that the model can analyze, along with the fields and schemas it will extract.
• Managing models created in your account by building, listing, deleting, and see the limit of custom models your account. See example Manage models.
• Copying a custom model from one Document Intelligence resource to another.
• Creating a composed model from a collection of existing built models.
• Listing document model operations associated with the Document Intelligence resource.

Sample code snippets are provided to illustrate using a DocumentModelAdministrationClient here.

Long-running operations

Long-running operations are operations that consist of an initial request sent to the service to start an operation, followed by polling the service at intervals to determine whether the operation has completed or failed, and if it has succeeded, to get the result.

Methods that build models, analyze values from documents, or copy and compose models are modeled as long-running operations. The client exposes a begin<MethodName> method that returns a SyncPoller or PollerFlux instance. Callers should wait for the operation to be completed by calling getFinalResult() on the returned operation from the begin<MethodName> method. Sample code snippets are provided to illustrate using long-running operations below.

Examples

The following section provides several code snippets covering some of the most common Document Intelligence tasks, including:

• Analyze Layout
• Use Prebuilt Models
• Build a Document Model
• Analyze Documents using a Custom Model
• Manage Your Models

Analyze Layout

Analyze text, table structures, and selection marks like radio buttons and check boxes, along with their bounding box coordinates from documents without the need to build a model.

File layoutDocument = new File("local/file_path/filename.png");
Path filePath = layoutDocument.toPath();
BinaryData layoutDocumentData = BinaryData.fromFile(filePath, (int) layoutDocument.length());

SyncPoller<AnalyzeResultOperation, AnalyzeResult> analyzeLayoutResultPoller =
    documentIntelligenceClient.beginAnalyzeDocument("prebuilt-layout",
        null,
        null,
        null,
        null,
        null,
        null,
        null,
        new AnalyzeDocumentRequest().setBase64Source(Files.readAllBytes(layoutDocument.toPath())));

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 box %s.%n",
            documentLine.getContent(),
            documentLine.getPolygon().toString()));

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

Use Prebuilt Models

Extract fields from select document types such as receipts, invoices, and identity documents using prebuilt models provided by the Document Intelligence service. Supported prebuilt models are:

• Analyze receipts using the prebuilt-receipt model (fields recognized by the service can be found here)
• Analyze invoices using the prebuilt-invoice model (fields recognized by the service can be found here).
• Analyze identity documents using the prebuilt-idDocuments model (fields recognized by the service can be found here).
• Analyze US W2 tax forms using the prebuilt-tax.us.w2 model. Supported fields.

For example, to analyze fields from a sales receipt, into the beginAnalyzeDocumentFromUrl method:

File sourceFile = new File("../documentintelligence/azure-ai-documentintelligence/src/samples/resources/"
    + "sample-forms/receipts/contoso-allinone.jpg");

SyncPoller<AnalyzeResultOperation, AnalyzeResult> analyzeReceiptPoller =
    documentIntelligenceClient.beginAnalyzeDocument("prebuilt-receipt",
        null,
        null,
        null,
        null,
        null,
        null,
        null,
        new AnalyzeDocumentRequest().setBase64Source(Files.readAllBytes(sourceFile.toPath())));

AnalyzeResult receiptResults = analyzeReceiptPoller.getFinalResult();

for (int i = 0; i < receiptResults.getDocuments().size(); i++) {
    Document analyzedReceipt = receiptResults.getDocuments().get(i);
    Map<String, DocumentField> receiptFields = analyzedReceipt.getFields();
    System.out.printf("----------- Analyzing receipt info %d -----------%n", i);
    DocumentField merchantNameField = receiptFields.get("MerchantName");
    if (merchantNameField != null) {
        if (DocumentFieldType.STRING == merchantNameField.getType()) {
            String merchantName = merchantNameField.getValueString();
            System.out.printf("Merchant Name: %s, confidence: %.2f%n",
                merchantName, merchantNameField.getConfidence());
        }
    }

    DocumentField merchantPhoneNumberField = receiptFields.get("MerchantPhoneNumber");
    if (merchantPhoneNumberField != null) {
        if (DocumentFieldType.PHONE_NUMBER == merchantPhoneNumberField.getType()) {
            String merchantAddress = merchantPhoneNumberField.getValuePhoneNumber();
            System.out.printf("Merchant Phone number: %s, confidence: %.2f%n",
                merchantAddress, merchantPhoneNumberField.getConfidence());
        }
    }

    DocumentField merchantAddressField = receiptFields.get("MerchantAddress");
    if (merchantAddressField != null) {
        if (DocumentFieldType.STRING == merchantAddressField.getType()) {
            String merchantAddress = merchantAddressField.getValueString();
            System.out.printf("Merchant Address: %s, confidence: %.2f%n",
                merchantAddress, merchantAddressField.getConfidence());
        }
    }

    DocumentField transactionDateField = receiptFields.get("TransactionDate");
    if (transactionDateField != null) {
        if (DocumentFieldType.DATE == transactionDateField.getType()) {
            LocalDate transactionDate = transactionDateField.getValueDate();
            System.out.printf("Transaction Date: %s, confidence: %.2f%n",
                transactionDate, transactionDateField.getConfidence());
        }
    }
}

For more information and samples using prebuilt models, see:

• Identity Documents
• Invoices
• Receipts sample

Build a document model

Build a machine-learned model on your own document type. The resulting model will be able to analyze values from the types of documents it was built on. Provide a container SAS url to your Azure Storage Blob container where you're storing the training documents. See details on setting this up in the service quickstart documentation.

Note

You can use the Document Intelligence Studio preview for creating a labeled file for your training forms. More details on setting up a container and required file structure can be found in here.

// Build custom document analysis model
String blobContainerUrl = "{SAS_URL_of_your_container_in_blob_storage}";
// The shared access signature (SAS) Url of your Azure Blob Storage container with your forms.
SyncPoller<DocumentModelBuildOperationDetails, DocumentModelDetails> buildOperationPoller =
    administrationClient.beginBuildDocumentModel(new BuildDocumentModelRequest("modelID", DocumentBuildMode.TEMPLATE)
        .setAzureBlobSource(new AzureBlobContentSource(blobContainerUrl)));

DocumentModelDetails documentModelDetails = buildOperationPoller.getFinalResult();

// Model Info
System.out.printf("Model ID: %s%n", documentModelDetails.getModelId());
System.out.printf("Model Description: %s%n", documentModelDetails.getDescription());
System.out.printf("Model created on: %s%n%n", documentModelDetails.getCreatedDateTime());

System.out.println("Document Fields:");
documentModelDetails.getDocTypes().forEach((key, documentTypeDetails) -> {
    documentTypeDetails.getFieldSchema().forEach((field, documentFieldSchema) -> {
        System.out.printf("Field: %s", field);
        System.out.printf("Field type: %s", documentFieldSchema.getType());
        System.out.printf("Field confidence: %.2f", documentTypeDetails.getFieldConfidence().get(field));
    });
});

Analyze Documents using a Custom Model

Analyze the key/value pairs and table data from documents. These models are built with your own data, so they're tailored to your documents. You should only analyze documents of the same doc type that the custom model was built on.

String documentUrl = "{document-url}";
String modelId = "{custom-built-model-ID}";
SyncPoller<AnalyzeResultOperation, AnalyzeResult> analyzeDocumentPoller = documentIntelligenceClient.beginAnalyzeDocument(modelId,
    "1",
    "en-US",
    StringIndexType.TEXT_ELEMENTS,
    Arrays.asList(DocumentAnalysisFeature.LANGUAGES),
    null,
    ContentFormat.TEXT,
    null,
    new AnalyzeDocumentRequest().setUrlSource(documentUrl));

AnalyzeResult analyzeResult = analyzeDocumentPoller.getFinalResult();

for (int i = 0; i < analyzeResult.getDocuments().size(); i++) {
    final Document analyzedDocument = analyzeResult.getDocuments().get(i);
    System.out.printf("----------- Analyzing custom document %d -----------%n", i);
    System.out.printf("Analyzed document has doc type %s with confidence : %.2f%n",
        analyzedDocument.getDocType(), analyzedDocument.getConfidence());
}

analyzeResult.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()));
});

// tables
List<DocumentTable> tables = analyzeResult.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();
}

Manage your models

Manage the models in your Document Intelligence account.

ResourceDetails resourceDetails = administrationClient.getResourceInfo();
System.out.printf("The resource has %s models, and we can have at most %s models.%n",
    resourceDetails.getCustomDocumentModels().getCount(), resourceDetails.getCustomDocumentModels().getLimit());

// Next, we get a paged list of all of our models
PagedIterable<DocumentModelDetails> customDocumentModels = administrationClient.listModels();
System.out.println("We have following models in the account:");
customDocumentModels.forEach(documentModelInfo -> {
    System.out.println();
    // get custom document analysis model info
    DocumentModelDetails documentModel = administrationClient.getModel(documentModelInfo.getModelId());
    System.out.printf("Model ID: %s%n", documentModel.getModelId());
    System.out.printf("Model Description: %s%n", documentModel.getDescription());
    System.out.printf("Model created on: %s%n", documentModel.getCreatedDateTime());
    if (documentModel.getDocTypes() != null) {
        documentModel.getDocTypes().forEach((key, documentTypeDetails) -> {
            documentTypeDetails.getFieldSchema().forEach((field, documentFieldSchema) -> {
                System.out.printf("Field: %s, ", field);
                System.out.printf("Field type: %s, ", documentFieldSchema.getType());
                if (documentTypeDetails.getFieldConfidence() != null) {
                    System.out.printf("Field confidence: %.2f%n",
                        documentTypeDetails.getFieldConfidence().get(field));
                }
            });
        });
    }
});

For more detailed examples, refer to samples.

Troubleshooting

Enable client logging

You can set the AZURE_LOG_LEVEL environment variable to view logging statements made in the client library. For example, setting AZURE_LOG_LEVEL=2 would show all informational, warning, and error log messages. The log levels can be found here: log levels.

Default HTTP Client

All client libraries by default use the Netty HTTP client. Adding the above dependency will automatically configure the client library to use the Netty HTTP client. Configuring or changing the HTTP client is detailed in the HTTP clients wiki.

Default SSL library

All client libraries, by default, use the Tomcat-native Boring SSL library to enable native-level performance for SSL operations. The Boring SSL library is an uber jar containing native libraries for Linux / macOS / Windows, and provides better performance compared to the default SSL implementation within the JDK. For more information, including how to reduce the dependency size, refer to the performance tuning section of the wiki.

Next steps

• Samples are explained in detail here.

Contributing

For details on contributing to this repository, see the contributing guide.

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create new Pull Request