🚀 Professional Browser OCR System

A high-performance, multi-engine OCR (Optical Character Recognition) system that runs entirely in your browser. No server uploads, no privacy concerns—just fast, secure text extraction.

✨ Key Features

• 🔒 Privacy First: All processing happens locally on your device. Images never leave your browser.
• ⚡ Multi-Engine Strategy: Choose the best engine for your needs:
  • Tesseract.js: The industry standard for general-purpose OCR.
  • Transformers.js (TrOCR): State-of-the-art AI accuracy using Transformer models.
  • eSearch-OCR (PaddleOCR): High-speed, high-accuracy engine optimized for Chinese/English mixed text.
• 🔋 Performance Optimized: Uses WebAssembly (WASM), Web Workers, and WebGPU acceleration for near-native speeds.
• 📦 Intelligent Caching: Heavy model files are cached in IndexedDB for instant subsequent loads.
• 🎨 Glassmorphism UI: A modern, clean interface with drag-and-drop, URL, and paste support.

🛠️ OCR Engines Comparison

Engine	Best For	Tech Stack	Model Size
Tesseract.js	General use, 100+ languages	WASM	~4.3 MB (eng/fast)
Transformers.js	Highest accuracy, modern AI	WebGPU / ONNX	~40-150 MB
eSearch-OCR	Chinese/English, complex layouts	ONNX Runtime	~7-10 MB

🚀 Getting Started

Prerequisites

• Modern Browser:
  • Basic Support (WASM/Workers): Chrome 92+, Firefox 79+, Safari 15.2+ (required for SharedArrayBuffer)
  • WebGPU Acceleration: Chrome 113+, Firefox 121+, Safari 17+
• Node.js: v18 or higher recommended

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/multi-engine-browser-ocr.git
   cd multi-engine-browser-ocr

2. Install dependencies:

   npm install

3. Start development server:

   npm run dev

🌍 Model Loading

Most engines download their models automatically from CDNs (Hugging Face or Tesseract CDN) on their first run and cache them locally.

eSearch-OCR Manual Setup (Optional for Offline)

By default, eSearch-OCR fetches models from Hugging Face. If you need to use it offline or host models yourself:

1. Download models from eSearch-OCR releases.
2. Place det.onnx, rec.onnx, and ppocr_keys_v1.txt into public/models/esearch/.

📂 Project Structure

• src/engines/: Implementation of different OCR strategies.
• src/utils/: Image processing, feature detection, and model caching.
• src/types/: Shared TypeScript interfaces.
• tests/: Comprehensive test suite using Vitest.
• docs/: Technical specifications and decision logs.

🧪 Development Commands

• npm run dev: Start Vite development server.
• npm run build: Build for production.
• npm test: Run all tests once.
• npm run test:watch: Run tests in watch mode.
• npm run lint: Check for code style issues.
• npm run format: Automatically fix formatting.

�️ Privacy & Security

This application is designed with security as a core principle:

• No Data Collection: Your images are processed entirely in the local browser context. No data is sent to external servers or APIs.
• Offline Capability: Once the models are cached, the engine can function without an active internet connection.
• Open Source: The entire pipeline is transparent and verifiable.

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md (if available) or simply open a Pull Request.

📖 Documentation

• Technical Specification: Deep dive into architecture and design.
• Decision Log: Rationale behind technical choices.