EasyOCR MCP tool

@WindoC

Basic information

About EasyOCR MCP tool

A Model Context Protocol (MCP) server that provides OCR capabilities using the EasyOCR library.

EasyOCR MCP tool config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "easyocr-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/your/project/easyocr-mcp",
        "run",
        "easyocr-mcp.py"
      ],
      "env": {
        "EASYOCR_LANGUAGES": "en,ch_tra,ja"
      }
    }
  }
}

EasyOCR MCP tool tools

Tool list not available yet

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Overview

What is EasyOCR MCP Server?

A Model Context Protocol (MCP) server that provides OCR capabilities using the open-source EasyOCR library. It allows AI agents to extract text from images (base64, files, or URLs) with support for over 80 languages.

How to use EasyOCR MCP Server?

Install dependencies with uv sync (optionally install PyTorch with GPU support). Configure language selection via the EASYOCR_LANGUAGES environment variable in your MCP client’s config.json. Run the server with uv run easyocr-mcp.py or mcp run easyocr-mcp.py, then invoke tools: ocr_image_base64, ocr_image_file, or ocr_image_url.

Key features of EasyOCR MCP Server

3 OCR Tools: base64, file, or URL input
Multi-language Support: 80+ languages with dynamic selection
Flexible Output: text-only or detailed results with coordinates and confidence
Performance Optimized: reader caching for better performance
Native EasyOCR Output: returns EasyOCR's original format

Use cases of EasyOCR MCP Server

Extracting text from scanned documents or photos
Reading text from images in multiple languages
Automating OCR from image URLs during web scraping
Processing base64-encoded images in chat or API workflows

Frequently asked questions

How do I select which languages to recognize?

Set the `EASYOCR_LANGUAGES` environment variable to a comma-separated list of language codes (e.g., `en,ch_sim,ja`). Default is English.

What output formats are available?

Set the `detail` parameter to `0` for text-only results (list of strings) or `1` for full details (list of tuples containing bounding box coordinates, text, and confidence score).

Does the server require a GPU?

GPU usage is determined at installation time based on your PyTorch installation. Install PyTorch with CUDA support to enable GPU acceleration; otherwise the server runs on CPU.

How do I process an image from a URL?

Use the `ocr_image_url` tool. The server will fetch the image from the URL and perform OCR automatically.

What are the threshold parameters for paragraph detection?

`width_ths` and `height_ths` control how close bounding boxes must be to be merged into a paragraph (default 0.7). The `paragraph` parameter enables paragraph detection (default false).

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