MCP server that converts file contents into compact, line-numbered PNG images for vision models to read, reducing token usage by roughly 7x for large files.
Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.
A Model Context Protocol server that gives AI assistants OCR with first-class accuracy handling and evaluation. It wraps three engines behind one interface and can score and compare them.
Provides structured OCR with verified bounding boxes using the space-ocr API. Allows extraction of structured fields from document images and listing of built-in templates.
Exposes local Umi-OCR v2 capabilities to AI agents via MCP, enabling image text extraction, batch OCR, PDF OCR, and status checks without manually starting the service.
Enables AI agents to recognize and extract text from images using PaddleOCR, supporting both file paths and base64 input with structured results including confidence scores and text positions.
Extracts text content from PDFs and images using Mistral's OCR API, enabling OCR capabilities in MCP-compatible clients like Cursor and Claude Desktop.
Enables text extraction from images and PDF files using the ZhipuAI GLM-OCR API. It allows users to process documents through local file paths or base64-encoded data for high-quality OCR results.
Enables OCR processing of local files and public URLs using Mistral OCR. Provides tools for health checks, key management, page listing, and generating Markdown output.
A Tesseract.js-based server that enables image-to-text recognition within MCP-compatible environments like Cursor. It supports multiple languages and common image formats, allowing users to extract text from local files using natural language commands.