Business Card OCR System

A production-level web application that extracts contact information from business card images using OCR (Optical Character Recognition) and generates vCard and CSV files.

Features

• 🖼️ Image Processing: Supports multiple image formats (JPG, PNG, BMP, TIFF, WebP)
• 🔍 OCR Technology: Uses Rapid OCR for accurate text extraction
• 🤖 AI-Powered Parsing: Leverages Google's Gemini-2.5-pro for intelligent data extraction
• 📱 vCard and CSV Generation: Creates standard vCard (.vcf) files for contact import and CSV files for database management.
• 🌐 Web Interface: User-friendly web application (JS/CSS/HTML)
• 📊 REST API: Full API for programmatic access
• ✅ Data Validation: Robust input validation and error handling

Architecture

The system consists of several modular services:

• OCR Service: Handles image preprocessing and text extraction using RapidOCR
• Parser Service: Uses Google Gemini-2.5-pro to extract structured data from raw OCR text
• vCard Service: Generates standard vCard format from structured data
• CSV Service: Generates CSV format from a structured database
• FastAPI Application: Provides a web interface and REST API endpoints

Installation

Prerequisites

1. Python 3.8+
2. Rapid OCR:

• Ubuntu/Debian: sudo apt-get install rapid-ocr
• macOS: brew install rapid-ocr
• Windows: Download from GitHub

3. OpenAI API Key (for structured data extraction)

Setup

1. Clone and install dependencies:

pip install -r requirements.txt

2. Configure environment: Create a .env file with:

GEMINI_API_KEY="YOUR API_KEY (FROM GOOGLE AI STUDIO)"

3. Run the application:

python main.py

The application will be available at http://localhost:8000

Usage

Web Interface

1. Navigate to http://localhost:8000
2. Upload a business card image
3. Click "Process Business Card"
4. View extracted information and download vCard and CSV files.

API Usage

Process Business Card

curl -X POST "http://localhost:8000/process-card" \
     -H "accept: application/json" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@business_card.jpg" \
     -F "include_vcard=true" \
     -F "include_raw_text=true"

Response Format

{
  "success": true,
  "raw_text": "Dev Dahiya\nSoftware Engineer\n...",
  "structured_data": {
    "first_name": "Dev",
    "last_name": "Dahiya",
    "company_name": "ZincPlus",
    "position": "Software Engineer",
    "email": "dev@zp.com",
    "mobile": "+1234567890",
    "website": "https://zincplus.in"
  },
  "vcard": "BEGIN:VCARD\nVERSION:3.0\n...",
  "error_message": null
}

Configuration

Key configuration options in config.py:

• MAX_FILE_SIZE: Maximum upload size (default: 10MB)
• ALLOWED_EXTENSIONS: Supported image formats
• GOOGLE_MODEL: Google model to use (default: gemini-2.5-pro)

Data Models

BusinessCardData

The system extracts the following fields:

• Required: first_name, last_name, company_name, position
• Optional: middle_name, department, mobile, telephone, email, address, extension, website, notes

vCard Format

Generated vCards follow the vCard 3.0 standard and include:

• Contact information (name, company, title)
• Phone numbers (mobile, work, extension)
• Email and website
• Business address
• Additional notes

Error Handling

The system includes comprehensive error handling:

• File validation: Size, format, and content checks
• OCR failures: Graceful handling of unreadable images
• API errors: Proper error responses with detailed messages
• Data validation: Pydantic models ensure data integrity

Performance Optimisation

• Image preprocessing: Automatic resizing and format conversion
• Efficient OCR: Optimised RapidOCR configuration for business cards
• Async operations: FastAPI async support for better concurrency
• Resource cleanup: Automatic temporary file cleanup

Deployment

Docker Deployment

Create a Dockerfile:

FROM python:3.9-slim

# Install system dependencies
RUN apt-get update && apt-get install -y \
    rapid-ocr \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Production Considerations

1. Environment Variables: Use proper secret management
2. File Storage: Consider cloud storage for uploaded files
3. Rate Limiting: Implement API rate limiting
4. Monitoring: Add logging and monitoring
5. Security: Implement authentication if needed
6. Load Balancing: Use reverse proxy (nginx) for production

API Documentation

Once running, visit http://localhost:8000/docs for interactive API documentation (Swagger UI).

Troubleshooting

Common Issues

1. RapidOCR not found:

   • Ensure Rapid is installed

2. GOOGLE API errors:

   • Verify API key is valid
   • Check API usage limits
   • Ensure sufficient credits

3. Image processing errors:

   • Check image format is supported
   • Verify the image is not corrupted
   • Ensure image contains readable text

4. Poor OCR results:

   • Try higher resolution images
   • Ensure good lighting and contrast
   • Avoid blurry or skewed images

License

This project is licensed under the MIT License.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Support

For issues and questions:

• Check the troubleshooting section
• Review API documentation
• Create an issue on GitHub