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Handwriting-OCR_handwriting-ocr-mcp-server
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Handwriting-OCR_handwriting-ocr-mcp-server

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2025-03-10

Mirror ofhttps://github.com/Handwriting-OCR/handwriting-ocr-mcp-server

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Handwriting OCR MCP Server

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A Model Context Protocol (MCP) Server for Handwriting OCR API.

Overview

The Handwriting OCR MCP Server enables integration between MCP clients and the Handwriting OCR service. This document outlines the setup process and provides a basic example of using the client.

This server allows you to upload images and PDF documents, check their status, and retrieve the OCR result as Markdown.

Tools

Transcription

  • Upload Document
  • Check Status
  • Get Text

Prerequisites

Before you begin, ensure you have the following:

Installation

Installing via Smithery

To install handwriting-ocr-mcp-server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @Handwriting-OCR/handwriting-ocr-mcp-server --client claude

Installing manually for Claude Desktop

To use the Handwriting OCR MCP Server in Claude Desktop application, use:

{
    "mcpServers": {
        "handwriting-ocr": {
            "command": "node",
            "args": [
                "/Users/mateo/Local/Code/MCP/handwriting-ocr/build/index.js"
            ],
            "env": {
                "API_TOKEN": "your-api-token",
            },
            "disabled": false,
            "autoApprove": []
        }
    }
}

Configuration

The Handwriting OCR MCP Server supports environment variables to be set for authentication and configuration:

  • API_TOKEN: Your API token.

You can find these values in the API settings dashboard on the Handwriting OCR Platform.

Support

Please refer to the Handwriting OCR API Documentation.

For support with the Handwriting OCR MCP Server, please submit a GitHub Issue.

About

Model Context Protocol (MCP) Server for Handwriting OCR Platform

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    Reviews

    1 (1)
    Avatar
    user_YdnCP1ih
    2025-04-16

    Handwriting-OCR_handwriting-ocr-mcp-server by MCP-Mirror is an exceptional tool for converting handwritten notes into digital text effortlessly. The accuracy and efficiency of this OCR server are impressive, making it a must-have for anyone dealing with handwritten documents. I highly recommend checking it out at https://github.com/MCP-Mirror/Handwriting-OCR_handwriting-ocr-mcp-server.