A comprehensive system for processing and evaluating Optical Mark Recognition (OMR) sheets with high accuracy and scalability.

• Backend API: FastAPI-based REST API for image processing and scoring
• ML Pipeline: CNN-based bubble classification with OpenCV preprocessing
• Frontend Dashboard: React-based web interface for uploads and results
• Database: PostgreSQL for data persistence and audit trails
• Reporting: PDF generation and analytics dashboard

• Horizontal scaling with load balancers
• Redis caching for template data
• Queue-based processing for high-volume uploads
• CDN for static assets and processed images

omr-platform/
├── backend/                 # FastAPI backend
├── frontend/               # React frontend
├── ml_models/              # CNN models and training
├── database/               # Database schemas and migrations
├── docs/                   # Documentation
├── tests/                  # Test suites
└── docker/                 # Docker configurations

1. Backend Setup:

   cd backend
   pip install -r requirements.txt
   uvicorn main:app --reload

2. Frontend Setup:

   cd frontend
   npm install
   npm start

3. Database Setup:

   docker-compose up -d postgres redis

• Throughput: 3000+ sheets per exam day
• Accuracy: <0.5% error tolerance
• Response Time: <2s per sheet processing
• Availability: 99.9% uptime

• Mobile camera support with lighting correction
• Multiple template version support
• Human-in-the-loop review system
• Real-time processing status
• Comprehensive audit logging
• Bulk export capabilities