"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.

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.

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.

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.

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.

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.

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 OCR processing of local files and public URLs using Mistral OCR. Provides tools for health checks, key management, page listing, and generating Markdown output.

Enables automated OCR and Thai tax invoice processing from PDFs and scanned images, including parsing key invoice fields, three-tier routing, SQLite storage, and human review workflows via MCP tools.

Enables MCP clients like Claude Code to extract text from images, screenshots, and scanned documents via PaddleOCR PP-OCRv6.

Enables document OCR processing via OcrPlane APIs, supporting document parsing, asynchronous polling, markdown extraction, and task management.

Enables users to extract text from scanned PDF pages and images using existing text layers or Tesseract OCR, returning lines and words with bounding boxes and confidence scores. It also supports searching pages, reading specific regions, and caching results so extracted numbers can be checked against the page.

Enables AI assistants to access and update personalized context data, creating persistent memory between sessions.