Aadhaar OCR Microservice

A FastAPI-based microservice for extracting information from Aadhaar cards using Optical Character Recognition (OCR).

Project Structure

aadhaar_ocr_fastapi/
├── app/
│   ├── __init__.py          # Package initialization
│   ├── main.py              # FastAPI application entry point
│   ├── routes/              # API route definitions
│   │   └── ocr.py          # OCR API endpoints
│   └── services/            # Business logic services
│       └── ocr_service.py  # OCR processing service
├── requirements.txt         # Project dependencies
README.md               # Project documentation

Quick Start

1. Install dependencies:

pip install -r requirements.txt
brew install tesseract       # Install Tesseract OCR engine

2. Run the service:

uvicorn app.main:app --reload

API Usage

Extract Aadhaar Info

POST /api/ocr/aadhaar

Upload an Aadhaar card image (PNG/JPEG) to extract:

• Name
• Aadhaar Number
• DOB
• Gender
• Address

Example Response:

{
    "status": "success",
    "data": {
        "name": "Suresh Kumar",
        "aadhaar_number": "XXXX XXXX XXXX",
        "dob": "1992-06-15",
        "gender": "Male",
        "address": "Full extracted address"
    }
}

Project Flow

1. Image Upload

   • User uploads an Aadhaar card image through the API endpoint
   • The image is received as a multipart form data

2. OCR Processing

   • The image is processed using PyTesseract OCR engine
   • Text is extracted from the image

3. Information Extraction

   • The extracted text is parsed to find specific patterns:
     • Name: Extracted from text above DOB or between DOB and Gender
     • Aadhaar Number: Using regex pattern (4 digits space 4 digits space 4 digits)
     • DOB: Using regex pattern (DD/MM/YYYY)
     • Gender: Looking for keywords like 'Male', 'Female', 'M', 'F'
     • Address: Extracted by identifying address keywords and parsing the text block

4. Data Cleaning

   • Text is cleaned to remove special characters
   • Address formatting is standardized
   • Personal information is validated against regex patterns

Technology Stack

• FastAPI: Modern, fast web framework for building APIs
• PyTesseract: Python wrapper for Google's Tesseract-OCR
• Pillow: Python Imaging Library for image processing
• Python-Multipart: For handling file uploads
• uvicorn: ASGI server implementation for running the application

Key Features

• Efficient OCR processing using Tesseract
• Robust text pattern matching for accurate information extraction
• Clean and maintainable code structure
• FastAPI's built-in request validation and error handling
• Easy to extend with additional OCR capabilities

Error Handling

The API includes comprehensive error handling for:

• Invalid image formats
• Missing image uploads
• OCR processing failures
• Pattern matching failures
• Invalid data formats

Security Considerations

• All Aadhaar numbers are masked in the response (XXXX XXXX XXXX)
• The service doesn't store any Aadhaar images or extracted data
• Input validation is performed at all levels
• Rate limiting can be added for production use

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request