Updates all project documentation to reflect that chart recognition is now fully enabled with PaddlePaddle 3.2.1+. Changes: - README.md: Remove Known Limitations section about chart recognition, update tech stack and prerequisites to include PaddlePaddle 3.2.1+, add WSL CUDA configuration notes - openspec/project.md: Add comprehensive chart recognition feature descriptions, update system requirements for GPU/CUDA support - openspec/changes/add-gpu-acceleration-support/tasks.md: Mark task 5.4 as completed with resolution details - openspec/changes/add-gpu-acceleration-support/proposal.md: Update Known Issues section to show chart recognition is now resolved - setup_dev_env.sh: Upgrade PaddlePaddle from 3.0.0 to 3.2.1+, add WSL CUDA library path configuration, add chart recognition API verification All documentation now accurately reflects: ✅ Chart recognition fully enabled ✅ PaddlePaddle 3.2.1+ with fused_rms_norm_ext API ✅ WSL CUDA path auto-configuration ✅ Comprehensive PP-StructureV3 capabilities 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
8.1 KiB
Tool_OCR
OCR Batch Processing System with Structure Extraction
A web-based solution to extract text, images, and document structure from multiple files efficiently using PaddleOCR-VL.
Features
- 🔍 Multi-Language OCR: Support for 109 languages (Chinese, English, Japanese, Korean, etc.)
- 📄 Document Structure Analysis: Intelligent layout analysis with PP-StructureV3
- 🖼️ Image Extraction: Preserve document images alongside text content
- 📑 Batch Processing: Process multiple files concurrently with progress tracking
- 📤 Multiple Export Formats: TXT, JSON, Excel, Markdown with images, searchable PDF
- 📋 Office Documents: DOC, DOCX, PPT, PPTX support via LibreOffice conversion
- 🚀 GPU Acceleration: Automatic CUDA GPU detection with graceful CPU fallback
- 🔧 Flexible Configuration: Rule-based output formatting
- 🌐 Translation Ready: Reserved architecture for future translation features
Tech Stack
Backend
- Framework: FastAPI 0.115.0
- OCR Engine: PaddleOCR 3.0+ with PaddleOCR-VL and PP-StructureV3
- Deep Learning: PaddlePaddle 3.2.1+ (GPU/CPU support)
- Database: MySQL via SQLAlchemy
- PDF Generation: Pandoc + WeasyPrint
- Image Processing: OpenCV, Pillow, pdf2image
- Office Conversion: LibreOffice (headless mode)
Frontend
- Framework: React 19 with TypeScript
- Build Tool: Vite 7
- Styling: Tailwind CSS v4 + shadcn/ui
- State Management: React Query + Zustand
- HTTP Client: Axios
Prerequisites
- OS: WSL2 Ubuntu 24.04
- Python: 3.12+
- Node.js: 24.x LTS
- MySQL: External database server (provided)
- GPU (Optional): NVIDIA GPU with CUDA 11.8+ for hardware acceleration
- PaddlePaddle 3.2.1+ requires CUDA 11.8, 12.3, or 12.6+
- WSL2 users: Ensure NVIDIA CUDA drivers are installed
Quick Start
1. Automated Setup (Recommended)
# Run automated setup script
./setup_dev_env.sh
This script automatically:
- Detects NVIDIA GPU and CUDA version (if available)
- Installs Python development tools (pip, venv, build-essential)
- Installs system dependencies (pandoc, LibreOffice, fonts, etc.)
- Installs Node.js (via nvm)
- Installs PaddlePaddle 3.2.1+ GPU version (if GPU detected) or CPU version
- Configures WSL CUDA library paths (for WSL2 GPU users)
- Installs other Python packages (PaddleOCR, PaddleX, etc.)
- Installs frontend dependencies
- Verifies GPU functionality and chart recognition API availability
2. Initialize Database
source venv/bin/activate
cd backend
alembic upgrade head
python create_test_user.py
cd ..
Default test user:
- Username:
admin - Password:
admin123
3. Start Development Servers
Backend (Terminal 1):
./start_backend.sh
Frontend (Terminal 2):
./start_frontend.sh
4. Access Application
- Frontend: http://localhost:5173
- API Docs: http://localhost:8000/docs
- Health Check: http://localhost:8000/health
Project Structure
Tool_OCR/
├── backend/ # FastAPI backend
│ ├── app/
│ │ ├── api/v1/ # API endpoints
│ │ ├── core/ # Configuration, database
│ │ ├── models/ # Database models
│ │ ├── services/ # Business logic
│ │ └── main.py # Application entry point
│ ├── alembic/ # Database migrations
│ └── tests/ # Test suite
├── frontend/ # React frontend
│ ├── src/
│ │ ├── components/ # UI components
│ │ ├── pages/ # Page components
│ │ ├── services/ # API services
│ │ └── stores/ # State management
│ └── public/ # Static assets
├── .env.local # Local development config
├── setup_dev_env.sh # Environment setup script
├── start_backend.sh # Backend startup script
└── start_frontend.sh # Frontend startup script
Configuration
Main config file: .env.local
# Database
MYSQL_HOST=mysql.theaken.com
MYSQL_PORT=33306
# Application ports
BACKEND_PORT=8000
FRONTEND_PORT=5173
# Token expiration (minutes)
ACCESS_TOKEN_EXPIRE_MINUTES=1440 # 24 hours
# Supported file formats
ALLOWED_EXTENSIONS=png,jpg,jpeg,pdf,bmp,tiff,doc,docx,ppt,pptx
# OCR settings
OCR_LANGUAGES=ch,en,japan,korean
MAX_OCR_WORKERS=4
# GPU acceleration (optional)
FORCE_CPU_MODE=false # Set to true to disable GPU even if available
GPU_MEMORY_FRACTION=0.8 # Fraction of GPU memory to use (0.0-1.0)
GPU_DEVICE_ID=0 # GPU device ID to use (0 for primary GPU)
GPU Acceleration
The system automatically detects and utilizes NVIDIA GPU hardware when available:
- Auto-detection: Setup script detects GPU and installs appropriate PaddlePaddle version
- Graceful fallback: If GPU is unavailable or fails, system automatically uses CPU mode
- Performance: GPU acceleration provides 3-10x speedup for OCR processing
- Configuration: Control GPU usage via
.env.localenvironment variables - WSL2 CUDA Setup: For WSL2 users, CUDA library paths are automatically configured in
~/.bashrc
Chart Recognition: Requires PaddlePaddle 3.2.0+ for full PP-StructureV3 chart recognition capabilities (chart type detection, data extraction, axis/legend parsing). The setup script installs PaddlePaddle 3.2.1+ which includes all required APIs.
Check GPU status and chart recognition availability at: http://localhost:8000/health
API Endpoints
Authentication
POST /api/v1/auth/login- User login
File Management
POST /api/v1/upload- Upload filesPOST /api/v1/ocr/process- Start OCR processingGET /api/v1/batch/{id}/status- Get batch status
Results & Export
GET /api/v1/ocr/result/{id}- Get OCR resultGET /api/v1/export/pdf/{id}- Export as PDF
Full API documentation: http://localhost:8000/docs
Supported File Formats
- Images: PNG, JPG, JPEG, BMP, TIFF
- Documents: PDF
- Office: DOC, DOCX, PPT, PPTX
Office files are automatically converted to PDF before OCR processing.
Development
Backend
source venv/bin/activate
cd backend
# Run tests
pytest
# Database migration
alembic revision --autogenerate -m "description"
alembic upgrade head
# Code formatting
black app/
Frontend
cd frontend
# Development server
npm run dev
# Build for production
npm run build
# Lint code
npm run lint
OpenSpec Workflow
This project follows OpenSpec for specification-driven development:
# View current changes
openspec list
# Validate specifications
openspec validate add-ocr-batch-processing
# View implementation tasks
cat openspec/changes/add-ocr-batch-processing/tasks.md
Roadmap
- Phase 0: Environment setup
- Phase 1: Core OCR backend (~98% complete)
- Phase 2: Frontend development (~92% complete)
- Phase 3: Testing & optimization
- Phase 4: Deployment automation
- Phase 5: Translation feature (future)
Documentation
- Development specs: openspec/project.md
- Implementation status: openspec/changes/add-ocr-batch-processing/STATUS.md
- Agent instructions: openspec/AGENTS.md
License
Internal project use
Notes
- First OCR run will download PaddleOCR models (~900MB)
- Token expiration is set to 24 hours by default
- Office conversion requires LibreOffice (installed via setup script)
- Development environment: WSL2 Ubuntu 24.04 with Python venv
- GPU acceleration: Automatically detected and enabled if NVIDIA GPU with CUDA 11.8+ is available
- PaddlePaddle version: System uses PaddlePaddle 3.2.1+ which includes full chart recognition support
- WSL GPU support: WSL2 CUDA library paths (
/usr/lib/wsl/lib) are automatically configured in~/.bashrc - Chart recognition: Fully enabled with PP-StructureV3 for chart type detection, data extraction, and structure analysis
- GPU status and chart recognition availability can be checked via
/healthAPI endpoint