
Azure Document Intelligence SDK
FreeBuild document analysis applications with ease.
Free · Opens the source repo
What Azure Document Intelligence SDK does
The Azure Document Intelligence (Form Recognizer) SDK for Java enables developers to create applications that analyze documents using Azure's powerful AI capabilities. With this SDK, you can extract information from various document types, including receipts, invoices, and business cards, leveraging prebuilt models that simplify the process of data extraction. The SDK provides a straightforward API for integrating document analysis into your Java applications, making it suitable for both new and experienced developers.
To get started, you simply need to add the SDK as a dependency in your project. The SDK includes clients for both document analysis and model administration, allowing you to build custom models tailored to your specific needs. The use of the AzureKeyCredential or DefaultAzureCredential simplifies authentication, ensuring secure access to Azure services.
The SDK supports a variety of prebuilt models that cater to different document formats. For instance, you can use the prebuilt-layout model to extract text and tables, or the prebuilt-receipt model for receipt data extraction. Additionally, the SDK allows for the analysis of documents from both local files and URLs, providing flexibility in how you handle document inputs. The results from the analysis are structured, making it easy to process and use the extracted data in your applications.
This SDK is ideal for developers looking to implement document analysis features in their applications without needing extensive machine learning expertise. Whether you are building a financial application that requires invoice processing or a CRM that needs to capture business card information, the Azure Document Intelligence SDK for Java offers the tools necessary to streamline these tasks.
When to use it
Use this SDK when you need to implement document analysis features in Java applications, especially for processing invoices, receipts, or other structured documents.
When not to use it
This SDK may not be suitable for applications that require real-time processing of documents or those needing advanced custom machine learning models beyond what is provided.
What you can build with it
Invoice Processing
Use the SDK to automate the extraction of key fields from invoices, reducing manual entry and errors.
Receipt Analysis
Quickly analyze receipts to capture merchant names, transaction dates, and itemized purchases for expense tracking.
Business Card Parsing
Integrate the SDK to extract contact information from business cards, streamlining CRM data entry.
How to install Azure Document Intelligence SDK
View source1. Install with the skills CLI
npx skills add sickn33/agentic-awesome-skills/azure-ai-formrecognizer-java --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by sickn33Azure Document Intelligence (Form Recognizer) SDK for Java
Build document analysis applications using the Azure AI Document Intelligence SDK for Java.
Installation
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-formrecognizer</artifactId>
<version>4.2.0-beta.1</version>
</dependency>
Client Creation
DocumentAnalysisClient
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClient;
import com.azure.ai.formrecognizer.documentanalysis.DocumentAnalysisClientBuilder;
import com.azure.core.credential.AzureKeyCredential;
DocumentAnalysisClient client = new DocumentAnalysisClientBuilder()
.credential(new AzureKeyCredential("{key}"))
.endpoint("{endpoint}")
.buildClient();
DocumentModelAdministrationClient
import com.azure.ai.formrecognizer.documentanalysis.administration.DocumentModelAdministrationClient;
import com.azure.ai.formrecognizer.documentanalysis.administration.DocumentModelAdministrationClientBuilder;
DocumentModelAdministrationClient adminClient = new DocumentModelAdministrationClientBuilder()
.credential(new AzureKeyCredential("{key}"))
.endpoint("{endpoint}")
.buildClient();
With DefaultAzureCredential
import com.azure.identity.DefaultAzureCredentialBuilder;
DocumentAnalysisClient client = new DocumentAnalysisClientBuilder()
.endpoint("{endpoint}")
.credential(new DefaultAzureCredentialBuilder().build())
.buildClient();
Prebuilt Models
| Model ID | Purpose |
|---|---|
prebuilt-layout | Extract text, tables, selection marks |
prebuilt-document | General document with key-value pairs |
prebuilt-receipt | Receipt data extraction |
prebuilt-invoice | Invoice field extraction |
prebuilt-businessCard | Business card parsing |
prebuilt-idDocument | ID document (passport, license) |
prebuilt-tax.us.w2 | US W2 tax forms |
Core Patterns
Extract Layout
import com.azure.ai.formrecognizer.documentanalysis.models.*;
import com.azure.core.util.BinaryData;
import com.azure.core.util.polling.SyncPoller;
import java.io.File;
File document = new File("document.pdf");
BinaryData documentData = BinaryData.fromFile(document.toPath());
SyncPoller<OperationResult, AnalyzeResult> poller =
client.beginAnalyzeDocument("prebuilt-layout", documentData);
AnalyzeResult result = poller.getFinalResult();
// Process pages
for (DocumentPage page : result.getPages()) {
System.out.printf("Page %d: %.2f x %.2f %s%n",
page.getPageNumber(),
page.getWidth(),
page.getHeight(),
page.getUnit());
// Lines
for (DocumentLine line : page.getLines()) {
System.out.println("Line: " + line.getContent());
}
// Selection marks (checkboxes)
for (DocumentSelectionMark mark : page.getSelectionMarks()) {
System.out.printf("Checkbox: %s (confidence: %.2f)%n",
mark.getSelectionMarkState(),
mark.getConfidence());
}
}
// Tables
for (DocumentTable table : result.getTables()) {
System.out.printf("Table: %d rows x %d columns%n",
table.getRowCount(),
table.getColumnCount());
for (DocumentTableCell cell : table.getCells()) {
System.out.printf("Cell[%d,%d]: %s%n",
cell.getRowIndex(),
cell.getColumnIndex(),
cell.getContent());
}
}
Analyze from URL
String documentUrl = "https://example.com/invoice.pdf";
SyncPoller<OperationResult, AnalyzeResult> poller =
client.beginAnalyzeDocumentFromUrl("prebuilt-invoice", documentUrl);
AnalyzeResult result = poller.getFinalResult();
Analyze Receipt
SyncPoller<OperationResult, AnalyzeResult> poller =
client.beginAnalyzeDocumentFromUrl("prebuilt-receipt", receiptUrl);
AnalyzeResult result = poller.getFinalResult();
for (AnalyzedDocument doc : result.getDocuments()) {
Map<String, DocumentField> fields = doc.getFields();
DocumentField merchantName = fields.get("MerchantName");
if (merchantName != null && merchantName.getType() == DocumentFieldType.STRING) {
System.out.printf("Merchant: %s (confidence: %.2f)%n",
merchantName.getValueAsString(),
merchantName.getConfidence());
}
DocumentField transactionDate = fields.get("TransactionDate");
if (transactionDate != null && transactionDate.getType() == DocumentFieldType.DATE) {
System.out.printf("Date: %s%n", transactionDate.getValueAsDate());
}
DocumentField items = fields.get("Items");
if (items != null && items.getType() == DocumentFieldType.LIST) {
for (DocumentField item : items.getValueAsList()) {
Map<String, DocumentField> itemFields = item.getValueAsMap();
System.out.printf("Item: %s, Price: %.2f%n",
itemFields.get("Name").getValueAsString(),
itemFields.get("Price").getValueAsDouble());
}
}
}
General Document Analysis
SyncPoller<OperationResult, AnalyzeResult> poller =
client.beginAnalyzeDocumentFromUrl("prebuilt-document", documentUrl);
AnalyzeResult result = poller.getFinalResult();
// Key-value pairs
for (DocumentKeyValuePair kvp : result.getKeyValuePairs()) {
System.out.printf("Key: %s => Value: %s%n",
kvp.getKey().getContent(),
kvp.getValue() != null ? kvp.getValue().getContent() : "null");
}
Custom Models
Build Custom Model
import com.azure.ai.formrecognizer.documentanalysis.administration.models.*;
String blobContainerUrl = "{SAS_URL_of_training_data}";
String prefix = "training-docs/";
SyncPoller<OperationResult, DocumentModelDetails> poller = adminClient.beginBuildDocumentModel(
blobContainerUrl,
DocumentModelBuildMode.TEMPLATE,
prefix,
new BuildDocumentModelOptions()
.setModelId("my-custom-model")
.setDescription("Custom invoice model"),
Context.NONE);
DocumentModelDetails model = poller.getFinalResult();
System.out.println("Model ID: " + model.getModelId());
System.out.println("Created: " + model.getCreatedOn());
model.getDocumentTypes().forEach((docType, details) -> {
System.out.println("Document type: " + docType);
details.getFieldSchema().forEach((field, schema) -> {
System.out.printf(" Field: %s (%s)%n", field, schema.getType());
});
});
Analyze with Custom Model
SyncPoller<OperationResult, AnalyzeResult> poller =
client.beginAnalyzeDocumentFromUrl("my-custom-model", documentUrl);
AnalyzeResult result = poller.getFinalResult();
for (AnalyzedDocument doc : result.getDocuments()) {
System.out.printf("Document type: %s (confidence: %.2f)%n",
doc.getDocType(),
doc.getConfidence());
doc.getFields().forEach((name, field) -> {
System.out.printf("Field '%s': %s (confidence: %.2f)%n",
name,
field.getContent(),
field.getConfidence());
});
}
Compose Models
List<String> modelIds = Arrays.asList("model-1", "model-2", "model-3");
SyncPoller<OperationResult, DocumentModelDetails> poller =
adminClient.beginComposeDocumentModel(
modelIds,
new ComposeDocumentModelOptions()
.setModelId("composed-model")
.setDescription("Composed from multiple models"));
DocumentModelDetails composedModel = poller.getFinalResult();
Manage Models
// List models
PagedIterable<DocumentModelSummary> models = adminClient.listDocumentModels();
for (DocumentModelSummary summary : models) {
System.out.printf("Model: %s, Created: %s%n",
summary.getModelId(),
summary.getCreatedOn());
}
// Get model details
DocumentModelDetails model = adminClient.getDocumentModel("model-id");
// Delete model
adminClient.deleteDocumentModel("model-id");
// Check resource limits
ResourceDetails resources = adminClient.getResourceDetails();
System.out.printf("Models: %d / %d%n",
resources.getCustomDocumentModelCount(),
resources.getCustomDocumentModelLimit());
Document Classification
Build Classifier
Map<String, ClassifierDocumentTypeDetails> docTypes = new HashMap<>();
docTypes.put("invoice", new ClassifierDocumentTypeDetails()
.setAzureBlobSource(new AzureBlobContentSource(containerUrl).setPrefix("invoices/")));
docTypes.put("receipt", new ClassifierDocumentTypeDetails()
.setAzureBlobSource(new AzureBlobContentSource(containerUrl).setPrefix("receipts/")));
SyncPoller<OperationResult, DocumentClassifierDetails> poller =
adminClient.beginBuildDocumentClassifier(docTypes,
new BuildDocumentClassifierOptions().setClassifierId("my-classifier"));
DocumentClassifierDetails classifier = poller.getFinalResult();
Classify Document
SyncPoller<OperationResult, AnalyzeResult> poller =
client.beginClassifyDocumentFromUrl("my-classifier", documentUrl, Context.NONE);
AnalyzeResult result = poller.getFinalResult();
for (AnalyzedDocument doc : result.getDocuments()) {
System.out.printf("Classified as: %s (confidence: %.2f)%n",
doc.getDocType(),
doc.getConfidence());
}
Error Handling
import com.azure.core.exception.HttpResponseException;
try {
client.beginAnalyzeDocumentFromUrl("prebuilt-receipt", "invalid-url");
} catch (HttpResponseException e) {
System.out.println("Status: " + e.getResponse().getStatusCode());
System.out.println("Error: " + e.getMessage());
}
Environment Variables
FORM_RECOGNIZER_ENDPOINT=https://<resource>.cognitiveservices.azure.com/
FORM_RECOGNIZER_KEY=<your-api-key>
Trigger Phrases
- "document intelligence Java"
- "form recognizer SDK"
- "extract text from PDF"
- "OCR document Java"
- "analyze invoice receipt"
- "custom document model"
- "document classification"
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Frequently asked questions about Azure Document Intelligence SDK
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