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ddddocr CAPTCHA Recognition Server

Recognize and analyze CAPTCHA images with high accuracy using advanced OCR, object detection, and slider matching capabilities. Enhance your AI toolchain with fast, ONNX-powered CAPTCHA recognition services. Seamlessly integrate CAPTCHA solving into your applications via a fully MCP-compatible server.

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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)

Visit Smithery.ai
Search for "ddddocr-captcha-recognition-ymeng98"
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.