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MCP OCR Server

by rjn32s

MCP 服务器，提供使用 Tesseract 的 OCR（光学字符识别）功能。需要安装 Tesseract OCR 引擎，支持的平台（macOS 通过 Homebrew，Linux 通过 apt/dnf/pacman）会自动安装。Windows 用户需手动安装 Tesseract。

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Repository-wide counts · Cached 2026-03-08

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Overview

The MCP OCR Server MCP server is a publicly available project. Review the upstream repository for installation instructions, supported tools, compatibility, permissions, and current maintenance status.

Configuration

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Open the MCP OCR Server repository to read the latest documentation.

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FROM THE SOURCE

Repository README

Build-time snapshot · Retrieved 2026-10-05

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MCP OCR Server

PyPI Downloads

A production-grade OCR server built using MCP (Model Context Protocol) that provides OCR capabilities through a simple interface.

Features

  • Extract text from images using Tesseract OCR
  • Support for multiple input types:
    • Local image files
    • Image URLs
    • Raw image bytes
  • Automatic Tesseract installation
  • Support for multiple languages
  • Production-ready error handling

Installation

# Using pip
pip install mcp-ocr

# Using uv
uv pip install mcp-ocr

Tesseract will be installed automatically on supported platforms:

  • macOS (via Homebrew)
  • Linux (via apt, dnf, or pacman)
  • Windows (manual installation instructions provided)

Usage

As an MCP Server

  1. Start the server:
python -m mcp_ocr
  1. Configure Claude for Desktop: Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
    "mcpServers": {
        "ocr": {
            "command": "python",
            "args": ["-m", "mcp_ocr"]
        }
    }
}

Available Tools

perform_ocr

Extract text from images:

# From file
perform_ocr("/path/to/image.jpg")

# From URL
perform_ocr("https://example.com/image.jpg")

# From bytes
perform_ocr(image_bytes)
get_supported_languages

List available OCR languages:

get_supported_languages()

Development

  1. Clone the repository:
git clone https://github.com/rjn32s/mcp-ocr.git
cd mcp-ocr
  1. Set up development environment:
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .
  1. Run tests:
pytest

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Security

  • Never commit API tokens or sensitive credentials
  • Use environment variables or secure credential storage
  • Follow GitHub's security best practices

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments