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

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.

Provides detailed character analysis for text, counting total characters, characters without spaces, letters, numbers, and symbols through a simple MCP tool interface.

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.

A lightweight server that provides detailed text analysis, counting total characters, characters without spaces, letters, numbers, and symbols for AI assistants like Claude Desktop and GitHub Copilot.

Extracts text from images using Tesseract OCR with support for local files, URLs, and raw image bytes. It provides production-grade OCR capabilities and multi-language support through the Model Context Protocol.

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.

Provides offline, high-accuracy OCR capabilities for images and PDFs using macOS's built-in Vision framework. Supports multi-language text extraction with intelligent block aggregation for tables and paragraphs, outputting structured JSON data suitable for document reconstruction.

Enables counting characters or bytes in text with options to include or exclude whitespace. Provides a simple tool for text analysis and length measurement.

Provides a tool to count occurrences of specific characters in text, serving as a simple example of building MCP servers with FastMCP and Python.

An optimized Model Context Protocol server for document OCR processing using Mistral AI with support for high-performance batch operations and async connection pooling. It enables efficient extraction of text and tables from local files or URLs into structured markdown and HTML formats while minimizing token costs.

Provides intelligent OCR and PDF processing capabilities that automatically detect whether PDFs contain digital text or scanned images and apply appropriate extraction methods. Supports text extraction, OCR processing, structure analysis, and batch operations.

Accurately counts Japanese text characters using grapheme clusters, properly handling surrogate pairs and combining characters through the Intl.Segmenter API.

A read-only MCP server for Cockpit CMS that lets AI agents explore content models, fields, and items via tools, without mutating data.

A project-aware AI design and code review server that uses dual reviewers via OpenRouter to catch context-blind errors in AI-generated code, optionally integrating with Serena for long-term project memory.

Enables unified code search and analysis over a monorepo, combining exact, semantic, and call graph queries with indexing, incremental updates, and impact analysis.

A comprehensive MCP server for managing PocketBase database schemas and migrations via REST API. It enables users to generate and execute migrations for creating, modifying, and deleting collections and fields directly through MCP-compatible clients.

Enables MCP clients to securely read, write, and maintain Frappe/ERPNext sites with role-based permissions, audit logging, and dry-run safeguards.

MCP server that provides tools, resources, and prompts to help LLMs build Svelte UI components for Q-SYS systems, including discovering components and controls from a Q-SYS Core.

Controls coding agents by enforcing structured implementation plans with PRs and commits as work units, requiring user review at each commit checkpoint to prevent agents from going off track. Includes a visualization dashboard for tracking progress in real-time.