How do I install the Azure Doc Ocr skill?

Run npx skillmds@latest add luokai0/azure-doc-ocr in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Azure Doc Ocr safe to use?

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Which AI agents work with Azure Doc Ocr?

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Is Azure Doc Ocr free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Azure Doc Ocr?

luokai0 (@luokai0) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Azure Doc Ocr by luokai0 — Agent Skill

Name: Azure Doc Ocr
Rating: 4.2 (5 reviews)
Author: luokai0

Azure Doc Ocr

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 (2024-11-30) with prebuilt models for various document types. Triggers: OCR, text extraction, Azure Document Intelligence, PDF OCR, image OCR, scanned documents, handwriting recognition, CJK text extraction, table extraction, invoice processing, receipt scanning, ID document recognition, document parsing, form extraction, Azure Form Recognizer

luokai0 Updated Sep 9, 2026 10 repo stars

File contents

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

---name: azure-doc-ocrdescription: 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 (2024-11-30) with prebuilt models for various document types. Triggers: OCR, text extraction, Azure Document Intelligence, PDF OCR, image OCR, scanned documents, handwriting recognition, CJK text extraction, table extraction, invoice processing, receipt scanning, ID document recognition, document parsing, form extraction, Azure Form Recognizer--- # 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: ```bash export AZURE_DOC_INTEL_ENDPOINT="https://your-resource.cognitiveservices.azure.com" export AZURE_DOC_INTEL_KEY="your-api-key" ``` ### 2. Single File OCR ```bash # 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 ```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](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 ```bash python scripts/ocr_extract.py scanned_contract.pdf --model prebuilt-read ``` ### Process invoices with structured output ```bash python scripts/ocr_extract.py invoice.pdf --model prebuilt-invoice --format json --output invoice_data.json ``` ### Batch process with layout analysis ```bash python scripts/batch_ocr.py ./reports/ --model prebuilt-layout --format markdown --workers 4 ``` ### Extract specific pages from large document ```bash python scripts/ocr_extract.py large_doc.pdf --pages 1,3-5,10 --format text ```

Frequently asked questions