DocumentAnalysisClient Class
- java.
lang. Object - com.
azure. ai. formrecognizer. documentanalysis. DocumentAnalysisClient
- com.
public final class DocumentAnalysisClient
This class provides a synchronous client to connect to the Form Recognizer Azure Cognitive Service.
This client provides synchronous methods to perform:
- Custom Document Analysis: Classification, extraction and analysis of data from forms and documents specific to distinct business data and use cases. Use the custom trained model by passing its modelId into the beginAnalyzeDocument(String modelId, BinaryData document) method.
- General Document Analysis: Extract text, tables, structure, and key-value pairs. Use general document model provided by the Form Recognizer service by passing modelId="rebuilt-document" into the beginAnalyzeDocument(String modelId, BinaryData document) method.
- Prebuilt Model Analysis: Analyze receipts, business cards, invoices, ID's, W2's and other documents with supported prebuilt models. Use the prebuilt receipt model provided by passing modelId="prebuilt-receipt" into the beginAnalyzeDocument(String modelId, BinaryData document) method.
- Layout Analysis: Extract text, selection marks, and tables structures, along with their bounding box coordinates, from forms and documents. Use the layout analysis model provided the service by passing modelId="prebuilt-layout" into the beginAnalyzeDocument(String modelId, BinaryData document) method.
- Polling and Callbacks: It includes mechanisms for polling the service to check the status of an analysis operation or registering callbacks to receive notifications when the analysis is complete.
This client also provides different methods based on inputs from a URL and inputs from a stream.
Note: This client only supports V2022_08_31 and newer. To use an older service version, FormRecognizerClient and FormTrainingClient.
Service clients are the point of interaction for developers to use Azure Form Recognizer. DocumentAnalysisClient is the synchronous service client and DocumentAnalysisAsyncClient is the asynchronous service client. The examples shown in this document use a credential object named DefaultAzureCredential for authentication, which is appropriate for most scenarios, including local development and production environments. Additionally, we recommend using managed identity for authentication in production environments. You can find more information on different ways of authenticating and their corresponding credential types in the Azure Identity documentation".
Sample: Construct a DocumentAnalysisAsyncClient with DefaultAzureCredential
The following code sample demonstrates the creation of a DocumentAnalysisClient, using the `DefaultAzureCredentialBuilder` to configure it.
DocumentAnalysisClient documentAnalysisClient = new DocumentAnalysisClientBuilder()
.endpoint("{endpoint}")
.credential(new DefaultAzureCredentialBuilder().build())
.buildClient();
Further, see the code sample below to use AzureKeyCredential for client creation.
DocumentAnalysisClient documentAnalysisClient = new DocumentAnalysisClientBuilder()
.credential(new AzureKeyCredential("{key}"))
.endpoint("{endpoint}")
.buildClient();
Method Summary
| Modifier and Type | Method and Description |
|---|---|
|
Sync |
beginAnalyzeDocument(String modelId, BinaryData document)
Analyzes data from documents using optical character recognition (OCR) using any of the prebuilt models or a custom-built analysis model. |
|
Sync |
beginAnalyzeDocument(String modelId, BinaryData document, AnalyzeDocumentOptions analyzeDocumentOptions, Context context)
Analyzes data from documents with optical character recognition (OCR) and semantic values from a given document using any of the prebuilt models or a custom-built analysis model. |
|
Sync |
beginAnalyzeDocumentFromUrl(String modelId, String documentUrl)
Analyzes data from documents with optical character recognition (OCR) and semantic values from a given document using any of the prebuilt models or a custom-built analysis model. |
|
Sync |
beginAnalyzeDocumentFromUrl(String modelId, String documentUrl, AnalyzeDocumentOptions analyzeDocumentOptions, Context context)
Analyzes data from documents with optical character recognition (OCR) and semantic values from a given document using any of the prebuilt models or a custom-built analysis model. |
|
Sync |
beginClassifyDocument(String classifierId, BinaryData document)
Classify a given document using a document classifier. |
|
Sync |
beginClassifyDocument(String classifierId, BinaryData document, Context context)
Classify a given document using a document classifier. |
|
Sync |
beginClassifyDocumentFromUrl(String classifierId, String documentUrl)
Classify a given document using a document classifier. |
|
Sync |
beginClassifyDocumentFromUrl(String classifierId, String documentUrl, Context context)
Classify a given document using a document classifier. |
Methods inherited from java.lang.Object
Method Details
beginAnalyzeDocument
public SyncPoller
Analyzes data from documents using optical character recognition (OCR) using any of the prebuilt models or a custom-built analysis model.
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support.
Code sample
File document = new File("{local/file_path/fileName.jpg}");
String modelId = "{custom_trained_model_id}";
byte[] fileContent = Files.readAllBytes(document.toPath());
documentAnalysisClient.beginAnalyzeDocument(modelId, BinaryData.fromBytes(fileContent))
.getFinalResult()
.getDocuments().stream()
.map(AnalyzedDocument::getFields)
.forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {
System.out.printf("Field text: %s%n", key);
System.out.printf("Field value data content: %s%n", documentField.getContent());
System.out.printf("Confidence score: %.2f%n", documentField.getConfidence());
}));
}
Parameters:
Returns:
beginAnalyzeDocument
public SyncPoller
Analyzes data from documents with optical character recognition (OCR) and semantic values from a given document using any of the prebuilt models or a custom-built analysis model.
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support.
Code sample
Analyze a document with configurable options.
File document = new File("{local/file_path/fileName.jpg}");
String modelId = "{custom_trained_model_id}";
byte[] fileContent = Files.readAllBytes(document.toPath());
documentAnalysisClient.beginAnalyzeDocument(modelId, BinaryData.fromBytes(fileContent),
new AnalyzeDocumentOptions().setPages(Arrays.asList("1", "3")), Context.NONE)
.getFinalResult()
.getDocuments().stream()
.map(AnalyzedDocument::getFields)
.forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {
System.out.printf("Field text: %s%n", key);
System.out.printf("Field value data content: %s%n", documentField.getContent());
System.out.printf("Confidence score: %.2f%n", documentField.getConfidence());
}));
Parameters:
Returns:
beginAnalyzeDocumentFromUrl
public SyncPoller
Analyzes data from documents with optical character recognition (OCR) and semantic values from a given document using any of the prebuilt models or a custom-built analysis model.
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support
Code sample
Analyze a document using the URL of the document.
String documentUrl = "{document_url}";
String modelId = "{custom_trained_model_id}";
documentAnalysisClient.beginAnalyzeDocumentFromUrl(modelId, documentUrl).getFinalResult()
.getDocuments().stream()
.map(AnalyzedDocument::getFields)
.forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {
System.out.printf("Field text: %s%n", key);
System.out.printf("Field value data content: %s%n", documentField.getContent());
System.out.printf("Confidence score: %.2f%n", documentField.getConfidence());
}));
Parameters:
Returns:
beginAnalyzeDocumentFromUrl
public SyncPoller
Analyzes data from documents with optical character recognition (OCR) and semantic values from a given document using any of the prebuilt models or a custom-built analysis model.
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support
Code sample
Analyze a document using the URL of the document with configurable options.
String documentUrl = "{document_url}";
String modelId = "{custom_trained_model_id}";
documentAnalysisClient.beginAnalyzeDocumentFromUrl(modelId, documentUrl).getFinalResult()
.getDocuments().stream()
.map(AnalyzedDocument::getFields)
.forEach(documentFieldMap -> documentFieldMap.forEach((key, documentField) -> {
System.out.printf("Field text: %s%n", key);
System.out.printf("Field value data content: %s%n", documentField.getContent());
System.out.printf("Confidence score: %.2f%n", documentField.getConfidence());
}));
Parameters:
Returns:
beginClassifyDocument
public SyncPoller
Classify a given document using a document classifier. For more information on how to build a custom classifier model, see
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support.
Code sample
File document = new File("{local/file_path/fileName.jpg}");
String classifierId = "{custom_trained_classifier_id}";
byte[] fileContent = Files.readAllBytes(document.toPath());
documentAnalysisClient.beginClassifyDocument(classifierId, BinaryData.fromBytes(fileContent))
.getFinalResult()
.getDocuments()
.forEach(analyzedDocument -> System.out.printf("Doc Type: %s%n", analyzedDocument.getDocType()));
Parameters:
Returns:
beginClassifyDocument
public SyncPoller
Classify a given document using a document classifier. For more information on how to build a custom classifier model, see
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support.
Code sample
File document = new File("{local/file_path/fileName.jpg}");
String classifierId = "{custom_trained_classifier_id}";
byte[] fileContent = Files.readAllBytes(document.toPath());
documentAnalysisClient.beginClassifyDocument(classifierId, BinaryData.fromBytes(fileContent), Context.NONE)
.getFinalResult()
.getDocuments()
.forEach(analyzedDocument -> System.out.printf("Doc Type: %s%n", analyzedDocument.getDocType()));
Parameters:
Returns:
beginClassifyDocumentFromUrl
public SyncPoller
Classify a given document using a document classifier. For more information on how to build a custom classifier model, see
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support
Code sample
Analyze a document using the URL of the document with configurable options.
String documentUrl = "{file_source_url}";
String classifierId = "{custom_trained_classifier_id}";
documentAnalysisClient.beginClassifyDocumentFromUrl(classifierId, documentUrl)
.getFinalResult()
.getDocuments()
.forEach(analyzedDocument -> System.out.printf("Doc Type: %s%n", analyzedDocument.getDocType()));
Parameters:
Returns:
beginClassifyDocumentFromUrl
public SyncPoller
Classify a given document using a document classifier. For more information on how to build a custom classifier model, see
The service does not support cancellation of the long running operation and returns with an error message indicating absence of cancellation support
Code sample
Analyze a document using the URL of the document with configurable options.
String documentUrl = "{file_source_url}";
String classifierId = "{custom_trained_classifier_id}";
documentAnalysisClient.beginClassifyDocumentFromUrl(classifierId, documentUrl, Context.NONE)
.getFinalResult()
.getDocuments()
.forEach(analyzedDocument -> System.out.printf("Doc Type: %s%n", analyzedDocument.getDocType()));
Parameters:
Returns:
