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