SkillHub

azure-doc-ocr

v1.0.0

Extract text and structured data from documents using Azure Document Intelligence (formerly Form Recognizer). Supports OCR for PDFs, images, scanned documents, handwritten text, CJK languages, tables, forms, invoices, receipts, ID documents, business cards, and tax forms. Uses the REST API v4.0 (202...

Sourced from ClawHub, Authored by HONGMIN LI

Installation

Please help me install the skill `azure-doc-ocr` from SkillHub official store. npx skills add Li-Hongmin/azure-doc-ocr

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