"Optical Character Recognition (OCR) Technology" matching MCP servers:

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

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.

Exposes ComfyUI-generated character avatars as tools, allowing control of expressions and poses via preset workflows with adjustable parameters.

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.

Enables AI agents to extract text from Brazilian court case PDFs and images fully offline, checking legibility, OCR-ing only the pages with corrupted fonts, and returning page-level text labelled as native text or OCR with sheet numbers ready for citation. Supports single pages, whole files, and batch folders of up to 50 PDFs per call.

Provides OCR capabilities for images and PDFs using Yandex Vision API. Supports multiple recognition models and output formats.

Enables AI agents to convert images and PDFs, including large scanned books, into Markdown through GLM-OCR, with support for long-image slicing, progress tracking, and asynchronous OCR tasks.

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.

OpenAI-compatible vision recognition MCP server that lets MCP clients see images by routing them to any OpenAI-compatible vision model.

Gives vision-less LLMs the ability to recognize clipboard screenshots and images by proxying to an OpenAI-compatible vision model.

Enables users to create character spritesheets from natural-language requests, preview animations, search clothing and tool items, and export engine-ready assets for Godot, Unity, and web.

Enables MCP clients to run OCR on local image files through a single long-lived PaddleOCR backend shared across clients, returning recognized lines as a Markdown table with optional coordinate and confidence columns.

Enables OCR-enabled PDF to Markdown conversion in Claude Code, with per-page inspection and hybrid handling of text, scanned, mixed, and blank pages via a local vision-model endpoint.

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 Semgrep as deterministic, rule-matching analysis tools over MCP so a code-review agent can scan only the lines changed in a diff, scan whole files on demand, and check analyzer availability, version, ruleset, and limits. Results are returned as compact per-finding lines with source snippets suitable as comment anchors, along with counts of suppressed out-of-diff matches.

Extracts text and images from PDFs and image files using the Mistral OCR API, with a security sandbox for file writes.

Extracts text from images and browser screenshots locally using Tesseract.js, without sending image data to the LLM.

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.

Analyzes images, audio, and video using Google's Gemini AI, with support for downloading videos from URLs such as Bilibili and YouTube.

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.