Files
OCR/README.md
egg d7e64737b7 feat: migrate to WSL Ubuntu native development environment
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>
2025-11-13 21:00:42 +08:00

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

# 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

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 files
  • POST /api/v1/ocr/process - Start OCR processing
  • GET /api/v1/batch/{id}/status - Get batch status

Results & Export

  • GET /api/v1/ocr/result/{id} - Get OCR result
  • GET /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

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