Intelligent OCR Document Processing System
AI OrientedAn enterprise-grade intelligent document processing platform providing automated OCR text extraction, document classification, structured data extraction, and entity recognition from invoices, contracts, and identity documents with real-time processing, multi-format support, and comprehensive document analysis capabilities.
Use Cases
Document management teams can upload scanned invoices, contracts, or forms through an intuitive drag-and-drop interface, automatically extracting structured data including invoice numbers, amounts, dates, vendor information, and line items. The platform processes documents through OCR text extraction, ML-based classification, and intelligent entity recognition, eliminating manual data entry and reducing processing time. Accounting departments can process batches of invoices, automatically extracting invoice numbers, total amounts, tax calculations, vendor details, and payment terms, with results displayed in editable form fields for verification and correction. Legal teams can extract key information from contracts including parties, dates, terms, and obligations, with document classification automatically identifying contract types. HR departments can process identity documents such as passports and driver's licenses, extracting personal information, dates of birth, identification numbers, and expiration dates. The platform provides real-time processing status updates, showing current processing step (normalization, OCR extraction, ML analysis) and estimated completion time. Users can view extracted data in structured format with editable fields, detected entities displayed as tags, tables rendered as interactive grids, and raw JSON output for integration with other systems. The system supports both synchronous processing for immediate results and asynchronous processing for scalable batch operations. Document classification automatically identifies document types (invoice, contract, identity document) based on content analysis, enabling automated routing and processing workflows. Entity extraction identifies and categorizes named entities including dates, organizations, monetary amounts, person names, and locations, providing comprehensive document understanding. Structured field extraction parses document-specific fields such as invoice numbers, total amounts, vendor names, and contract parties, enabling automated data entry and validation. Table detection identifies and extracts tabular data from documents, preserving structure and enabling data editing and export. The platform includes a sample invoice PDF generator for testing and demonstration, allowing users to quickly test system capabilities without requiring actual documents. Results can be exported as JSON for integration with other systems, with editable fields and tables preserved in the export. Perfect for document management teams digitizing paper documents, accounting departments processing invoices, legal teams extracting contract data, HR departments processing identity documents, and organizations requiring automated document data extraction.
Key Features
- OCR Text Extraction
- Multi-Format Document Support
- PDF Document Processing
- Image Preprocessing and Normalization
- Deskewing and Noise Reduction
- Document Classification
- Invoice Recognition and Processing
- Contract Analysis
- Identity Document Processing
- Named Entity Recognition
- Date Extraction
- Organization Name Extraction
- Monetary Amount Extraction
- Person Name Extraction
- Location Extraction
- Structured Field Extraction
- Invoice Number Parsing
- Total Amount Extraction
- Vendor Information Extraction
- Table Detection and Extraction
- Interactive Table Editing
- Real-Time Processing Status
- Asynchronous Task Processing
- Synchronous Processing Mode
- Progress Tracking
- Result Visualization
- Editable Field Display
- Entity Tag Visualization
- Raw JSON Export
- Sample Invoice PDF Generator
- Drag-and-Drop File Upload
- Multiple File Format Support
- Responsive User Interface
- Modern CSS Design
- Interactive Data Editing
- Comprehensive Error Handling
- RESTful API
- CORS Support
- Docker Containerization
- System Dependency Management
- Automated PDF Generation
- Environment Configuration
- Health Check Endpoints
- Comprehensive Logging
- Scalable Architecture
Architecture
Built with Python 3.13 and FastAPI 0.123+ for high-performance async backend services following microservices architecture principles with modular components for preprocessing, OCR extraction, ML analysis, and task orchestration. The backend implements RESTful APIs with async/await support, structured route handlers, service layers containing business logic, and clean separation of concerns. The architecture uses a pipeline-based pattern with dedicated modules for image preprocessing, OCR text extraction, ML analysis, and result formatting. The preprocessing module handles image normalization using OpenCV, performing deskewing, noise reduction, binarization, and format conversion (PDF to image using poppler-utils). The OCR engine integrates with Tesseract OCR for text extraction, providing detailed layout information, confidence scores, and structured text blocks. The ML processor uses spaCy NLP models for document classification, named entity recognition, and structured field extraction, processing OCR results to identify document types, extract entities, and parse structured fields. The frontend is built with modern HTML5, CSS3, and vanilla JavaScript, featuring responsive design with CSS Grid and Flexbox, smooth animations, and accessible components. The UI includes drag-and-drop file upload, real-time status updates, interactive data visualization, and comprehensive result display. API communication is handled through Fetch API with proper error handling, loading states, and response formatting. The platform implements CORS-enabled cross-origin resource sharing for secure API access. The task processing system uses Celery with Redis broker for asynchronous job execution, enabling scalable background processing, progress tracking, and result retrieval. The system also supports synchronous processing mode when async infrastructure is unavailable. The deployment uses Docker containerization with multi-stage builds, system dependency installation (Tesseract OCR, poppler-utils, OpenGL libraries), and optimized Python package management. The Dockerfile installs system dependencies including tesseract-ocr, poppler-utils, and OpenGL libraries, then installs Python dependencies from requirements.txt. The platform includes automated sample invoice PDF generation using reportlab for testing and demonstration purposes. The architecture supports horizontal scaling through stateless API design and implements proper error handling, logging, and monitoring capabilities. The backend service runs on configurable ports with automatic port assignment support, while the frontend serves static files and templates through FastAPI's static file mounting. The platform implements proper error handling throughout the pipeline, with graceful fallbacks and informative error messages.
Security & Performance
Security is implemented through multiple layers including CORS-enabled API access with configurable origin policies, secure file upload handling with proper validation and sanitization, and input validation throughout the application. The platform implements secure file storage with isolated upload directories, preventing path traversal attacks and unauthorized file access. The backend uses FastAPI security best practices including request validation, proper error handling without exposing sensitive information, and structured logging. File uploads are validated for supported formats (PDF, PNG, JPG, JPEG, TIFF) and size limits, preventing malicious file uploads and resource exhaustion. The platform supports environment-based configuration with secure defaults, allowing API keys, Redis URLs, and service endpoints to be configured through environment variables. OCR and ML processing operations are executed with proper isolation, using temporary file handling and cleanup to prevent resource leaks. The system implements proper error handling with informative messages for users while preventing information leakage about internal system architecture. Performance is optimized through efficient FastAPI async endpoints with async/await support for concurrent request handling, leveraging Python's asyncio for high-performance I/O operations. The OCR processing pipeline uses optimized Tesseract configuration with appropriate PSM (page segmentation mode) and OEM (OCR engine mode) settings for accurate text extraction. Image preprocessing uses OpenCV with optimized algorithms for deskewing and noise reduction, reducing processing time while maintaining accuracy. PDF conversion uses poppler-utils with optimized DPI settings (150 DPI) for balance between quality and processing speed. The ML processing uses spaCy models loaded once at startup, providing fast inference for document classification and entity extraction. The platform implements efficient task processing with Celery workers for parallel document processing, enabling scalable throughput for batch operations. File handling uses efficient streaming for large file uploads, preventing memory exhaustion. The architecture supports horizontal scaling through stateless API design and async task processing, allowing multiple workers to handle increased load. The frontend uses efficient DOM manipulation and event handling, providing responsive user experience with smooth animations and transitions. API responses are structured with proper JSON formatting and efficient data structures, reducing payload sizes and improving network performance. The platform implements proper caching strategies for ML models and static assets, reducing load times and improving responsiveness.
Development & Deployment
The application is built using Python 3.13 for backend services with FastAPI framework, async/await support, and comprehensive type hints. Development workflow uses pip for dependency management with requirements.txt for reproducible builds, with clean project structure separating preprocessing, OCR, ML processing, and web application modules. The backend follows Python best practices including type hints, proper error handling, structured logging, and idiomatic Python code patterns. The preprocessing module uses OpenCV and NumPy for image processing, implementing deskewing, noise reduction, and format conversion. The OCR engine integrates with Tesseract OCR through pytesseract Python bindings, providing text extraction with layout information and confidence scores. The ML processor uses spaCy NLP models for document classification and entity extraction, with model loading at startup for efficient inference. The web application uses FastAPI with Jinja2 templating for HTML rendering, static file serving, and RESTful API endpoints. The frontend uses modern HTML5, CSS3, and vanilla JavaScript with responsive design, drag-and-drop file upload, and real-time status updates. Component architecture follows separation of concerns with modular JavaScript functions for upload handling, status polling, result display, and data export. The platform includes comprehensive API documentation through RESTful endpoint design following OpenAPI standards. For production deployment, the application uses Docker containerization with optimized multi-stage builds, system dependency installation, and automated PDF generation. The Dockerfile installs system dependencies including tesseract-ocr, poppler-utils, and OpenGL libraries, then installs Python dependencies from requirements.txt. The deployment includes automated sample invoice PDF generation using reportlab for testing and demonstration purposes. The platform supports Railway deployment with automatic port assignment, environment variable configuration, and health check endpoints. Environment configuration uses Railway's environment variables for Redis URLs, API keys, processing modes, and deployment settings. The backend service runs on configurable ports with Railway's automatic port assignment and supports both synchronous and asynchronous processing modes. The deployment includes proper error handling, logging, and monitoring capabilities, with comprehensive logging for debugging and troubleshooting. The platform supports zero-downtime deployments through Railway's rolling update strategy and includes comprehensive documentation for setup, deployment, and configuration. The codebase structure enables unified development workflows with clear module separation and shared utilities.