從 Docker/macOS+Conda 部署遷移到 WSL2 Ubuntu 原生開發環境 主要變更: - 移除所有 Docker 相關配置檔案 (Dockerfile, docker-compose.yml, .dockerignore 等) - 移除 macOS/Conda 設置腳本 (SETUP.md, setup_conda.sh) - 新增 WSL Ubuntu 自動化環境設置腳本 (setup_dev_env.sh) - 新增後端/前端快速啟動腳本 (start_backend.sh, start_frontend.sh) - 統一開發端口配置 (backend: 8000, frontend: 5173) - 改進資料庫連接穩定性(連接池、超時設置、重試機制) - 更新專案文檔以反映當前 WSL 開發環境 Technical improvements: - Database connection pooling with health checks and auto-reconnection - Retry logic for long-running OCR tasks to prevent DB timeouts - Extended JWT token expiration to 24 hours - Support for Office documents (pptx, docx) via LibreOffice headless - Comprehensive system dependency installation in single script Environment: - OS: WSL2 Ubuntu 24.04 - Python: 3.12 (venv) - Node.js: 24.x LTS (nvm) - Backend Port: 8000 - Frontend Port: 5173 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
5.8 KiB
5.8 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
- 🔧 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
- 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)
Quick Start
1. Automated Setup (Recommended)
# Run automated setup script
./setup_dev_env.sh
This script automatically installs:
- Python development tools (pip, venv, build-essential)
- System dependencies (pandoc, LibreOffice, fonts, etc.)
- Node.js (via nvm)
- Python packages
- Frontend dependencies
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
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