ymeng98/ddddocr-captcha-mcp

ddddocr CAPTCHA Recognition MCP Server

A powerful MCP server for CAPTCHA recognition using ddddocr library, providing advanced text OCR, object detection, and slider matching capabilities.

Features

• 🔤 Text OCR Recognition - Recognize text content from CAPTCHA images
• 🎯 Object Detection - Detect target objects in CAPTCHA images
• 🔄 Slider Matching - Match slider CAPTCHA positions with high accuracy
• ⚡ High Performance - Built on ONNX runtime for fast processing
• 🔌 MCP Compatible - Fully compatible with Model Context Protocol

Installation & Usage

From Smithery (Recommended)

1. Visit Smithery.ai
2. Search for "ddddocr-captcha-recognition-ymeng98"
3. Install with one click to your AI toolchain

Local Development

# Clone and setup
git clone <repository-url>
cd ddddocr-captcha
npm install

# Install Python dependencies
pip install -r requirements.txt

# Run development server
npm run dev

Tools Available

ocr_recognize

Recognize text content from CAPTCHA images.

Parameters:

• image_base64 (optional): Base64 encoded image data
• image_path (optional): Path to image file

detect_objects

Detect target objects in CAPTCHA images.

Parameters:

• image_base64 (optional): Base64 encoded image data
• image_path (optional): Path to image file

match_slider

Match slider CAPTCHA position.

Parameters:

• target_base64 (optional): Target image base64 encoded
• background_base64 (optional): Background image base64 encoded
• target_path (optional): Target image file path
• background_path (optional): Background image file path

health_check

Check ddddocr service health status.

Technical Stack

• Core Recognition: ddddocr library
• Image Processing: OpenCV, Pillow
• Protocol: Model Context Protocol (MCP)
• Runtime: TypeScript (Node.js) + Python backend
• Models: ONNX-based neural networks

License

MIT License

Support

For issues and questions, please visit our GitHub repository or contact the maintainer.