Azure Document Intelligence OCR
Extract text and structured data from documents using Azure Document Intelligence REST API.
Quick Start
1. Environment Setup
Set your Azure Document Intelligence credentials:
export AZURE_DOC_INTEL_ENDPOINT="https://your-resource.cognitiveservices.azure.com"
export AZURE_DOC_INTEL_KEY="your-api-key"
2. Single File OCR
# Basic text extraction from PDF
python scripts/ocr_extract.py document.pdf
# Extract with layout (tables, structure)
python scripts/ocr_extract.py document.pdf --model prebuilt-layout --format markdown
# Process invoice
python scripts/ocr_extract.py invoice.pdf --model prebuilt-invoice --format json
# OCR from URL
python scripts/ocr_extract.py --url "https://example.com/document.pdf"
# Save output to file
python scripts/ocr_extract.py document.pdf --output result.txt
# Extract specific pages
python scripts/ocr_extract.py document.pdf --pages 1-3,5
3. Batch Processing
# Process all documents in a folder
python scripts/batch_ocr.py ./documents/
# Custom output directory and format
python scripts/batch_ocr.py ./documents/ --output-dir ./extracted/ --format markdown
# Use layout model with 8 workers
python scripts/batch_ocr.py ./documents/ --model prebuilt-layout --workers 8
# Filter specific extensions
python scripts/batch_ocr.py ./documents/ --ext .pdf,.png
Model Selection Guide
| Document Type | Recommended Model | Use Case |
|---|---|---|
| General text | prebuilt-read | Pure text extraction, any document |
| Structured docs | prebuilt-layout | Tables, forms, paragraphs, figures |
| Invoices | prebuilt-invoice | Vendor info, line items, totals |
| Receipts | prebuilt-receipt | Merchant, items, totals, dates |
| IDs/Passports | prebuilt-idDocument | Identity documents |
| Business cards | prebuilt-businessCard | Contact information |
| W-2 forms | prebuilt-tax.us.w2 | US tax documents |
| Insurance cards | prebuilt-healthInsuranceCard.us | Health insurance info |
See references/models.md for detailed model documentation.
Supported Input Formats
- PDF:
.pdf(including scanned PDFs) - Images:
.png,.jpg,.jpeg,.tiff,.bmp - URLs: Direct links to documents
Output Formats
- text: Plain text concatenation of all extracted content
- markdown: Structured output with headers and tables (best with layout model)
- json: Raw API response with full extraction details
Features
- Handwriting Recognition: Extracts handwritten text alongside printed text
- CJK Support: Full support for Chinese, Japanese, Korean characters
- Table Extraction: Preserves table structure (use layout model)
- Multi-page Processing: Handles documents with multiple pages
- Concurrent Processing: Batch script supports parallel processing
- URL Input: Process documents directly from URLs
Environment Variables
| Variable | Required | Description |
|---|---|---|
AZURE_DOC_INTEL_ENDPOINT | Yes | Azure Document Intelligence endpoint URL |
AZURE_DOC_INTEL_KEY | Yes | API subscription key |
Error Handling
- Invalid credentials: Check endpoint URL and API key
- Unsupported format: Ensure file extension matches supported types
- Timeout: Large documents may need longer processing (max 300s)
- Rate limiting: Reduce concurrent workers for batch processing
Examples
Extract text from scanned PDF
python scripts/ocr_extract.py scanned_contract.pdf --model prebuilt-read
Process invoices with structured output
python scripts/ocr_extract.py invoice.pdf --model prebuilt-invoice --format json --output invoice_data.json
Batch process with layout analysis
python scripts/batch_ocr.py ./reports/ --model prebuilt-layout --format markdown --workers 4
Extract specific pages from large document
python scripts/ocr_extract.py large_doc.pdf --pages 1,3-5,10 --format text
