Gemini OCR MCP Server

This project provides a simple yet powerful OCR (Optical Character Recognition) service through a FastMCP server, leveraging the capabilities of the Google Gemini API. It allows you to extract text from images either by providing a file path or a base64 encoded string.

Objective

Extract the text from the following image:

and convert it to plain text, e.g., fbVk

Features

• File-based OCR: Extract text directly from an image file on your local system.

• Base64 OCR: Extract text from a base64 encoded image string.

• Easy to Use: Exposes OCR functionality as simple tools in an MCP server.

• Powered by Gemini: Utilizes Google's advanced Gemini models for high-accuracy text recognition.

Prerequisites

• Python 3.8 or higher

• A Google Gemini API Key. You can obtain one from Google AI Studio.

Setup and Installation

1. Clone the repository:

   git clone https://github.com/WindoC/gemini-ocr-mcp cd gemini-ocr-mcp

2. Create and activate a virtual environment:

   # Install uv standalone if needed ## On macOS and Linux. curl -LsSf https://astral.sh/uv/install.sh | sh ## On Windows. powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

3. Install the required dependencies:

   uv sync

MCP Configuration Example

If you are running this as a server for a parent MCP application, you can configure it in your main MCP config.json.

Windows Example:

Linux/macOS Example:

Note: Remember to replace the placeholder paths with the absolute path to your project directory.

Tools Provided

ocr_image_file

Performs OCR on a local image file.

• Parameter: image_file (string): The absolute or relative path to the image file.

• Returns: (string) The extracted text from the image.

ocr_image_base64

Performs OCR on a base64 encoded image.

• Parameter: base64_image (string): The base64 encoded string of the image.

• Returns: (string) The extracted text from the image.