Handwriting OCR Handwriting Ocr MCP Server

The MCP server for handwriting OCR, which integrates with the Handwriting OCR API and supports uploading image/PDF documents, checking status, and obtaining OCR recognition results.

Image and video processing Education and learning tools #Handwriting Recognition #OCR Conversion #Document Processing Local .TypeScript

rating : 2 points

downloads : 9

update time : 2025-04-28

What is Handwriting OCR MCP Server?

Handwriting OCR MCP Server is a tool that connects Model Context Protocol (MCP) clients with Handwriting OCR services. It allows you to upload images and PDF files, check the processing status, and obtain OCR results in Markdown format.

How to use Handwriting OCR MCP Server?

By installing the MCP server and setting environment variables, you can easily integrate the Handwriting OCR service into your application. Simply upload the file, wait for the processing to complete, and then obtain the OCR results.

Applicable Scenarios

Handwriting OCR MCP Server is suitable for applications that require automatic recognition of handwritten text, such as educational platforms, archive management systems, and customer service tools.

Main Features

Upload DocumentsSupports uploading images and PDF files for OCR processing.

Check StatusTrack the processing progress of uploaded documents in real-time.

Get TextExtract text in Markdown format from processed documents.

Advantages and Limitations

Advantages

Easy to use and quickly integrate the Handwriting OCR service.

Supports multiple file formats, including images and PDFs.

Provides OCR results in Markdown format for easy further processing.

Limitations

Requires an API token for authentication.

May have a slower processing speed for large files, depending on the network conditions.

How to Use

Install the MCP Server

Install Handwriting OCR MCP Server through the Smithery tool or manual configuration.

Set the API Token

Add your Handwriting OCR API token to the configuration file.

Upload Documents

Use the client tool to upload the image or PDF file to be processed.

Usage Examples

Example 1: Upload a PDF FileUpload a PDF file to the Handwriting OCR service and obtain the OCR results in Markdown format.

Example 2: Obtain OCR ResultsQuery the completed OCR processing results using the task ID.

Frequently Asked Questions

Does Handwriting OCR MCP Server support batch file uploads?

How to ensure the security of uploaded files?

Do the OCR results support other formats?

Related Resources

Handwriting OCR API Documentation

Details the API functions and usage methods.

GitHub Repository

The place to view the source code and submit issues.

Installation Guide Video

Watch the tutorial on how to quickly install the MCP server.

🚀 Handwriting OCR MCP Server

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

🚀 Quick Start

The Handwriting OCR MCP server enables the integration of MCP clients with the Handwriting OCR service. This document outlines the installation process and provides a simple usage example for the client. The server allows you to upload images and PDF documents, check their status, and obtain OCR results in Markdown format.

✨ Features

Transcription

Upload documents
Check status
Retrieve text

📦 Installation

Prerequisites

Before you begin, ensure that:

Node.js (version 18.x or higher is recommended) is installed on your system.
You have an active account on the Handwriting OCR platform and a valid API token.

Automatic Installation via Smithery

To automatically install the handwriting-ocr-mcp-server for Claude Desktop through Smithery, use the following command:

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

Manual Installation in Claude Desktop

To use the Handwriting OCR MCP server in the Claude Desktop application, use the following command:

{
    "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": []
        }
    }
}