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

```bash
# 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

azure-doc-ocr

Azure Document Intelligence OCR

Quick Start

1. Environment Setup

2. Single File OCR