"How to extract text from images" matching MCP servers:

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

Enables fast text classification using Model2Vec static embeddings with 10 default categories (technology, business, health, etc.), supports custom category management, batch processing, and provides both local and remote deployment options.

An MCP server that provides a tool to extract text content from local PDF files, supporting both standard PDF reading and OCR capabilities with optional page selection.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Algorithmic text and NLP analysis server providing sentiment, readability, keyword extraction, language detection, stats, and more via REST and MCP.

An AI-powered server that analyzes uploaded images and automatically generates explanatory text (docent) for various images.

Enables parsing of raw resume text into structured JSON format with organized sections for skills, experience, education, and projects. Uses Google's Gemini AI model to extract and categorize resume information from unstructured text input.

Provides comprehensive text analysis capabilities including character counting, word statistics, character type analysis, and text length validation for Korean and English text. Supports AI agents in analyzing and validating text content with detailed statistics and Unicode support.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

MCP server for Differino that lets AI agents compare PDF, DOCX, and TXT documents, with tools for visual and text diffs, summaries, exports, and account balance checks.

MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.

Measures AI-detection risk in text and provides a line-by-line rewrite plan to lower the score.

Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.

A Model Context Protocol server that distills documents from multiple sources into HTML articles and Obsidian notes, with features like key element detection, image filtering, and async task management.

Enables LLMs to interact with Fathom.video API for managing meeting recordings, retrieving transcripts and AI-generated summaries, searching meeting content, and accessing analytics and team data.

Integrates Vale prose linting into AI coding assistants, enabling users to check text files for style and grammar issues using Vale's powerful linting engine. Provides automated style feedback with smart configuration discovery and rich formatted results.

Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.

Enables AI agents and MCP clients to browse Roomtone meeting notes, add YouTube or pasted-text sources, generate summaries, ask questions answered from transcripts, and retrieve raw transcript text. Recording itself stays in the Roomtone app, so the server only reads and adds to existing meetings.

23 developer & data API tools for AI agents - IP/DNS/WHOIS/SSL lookups, web scraping & screenshots, text AI (summarize, translate, sentiment, grammar, redact), and dev utilities (hash, UUID, QR, JWT, cron, IBAN/VAT/email validation, breach check).

MCP server that fetches YouTube video transcripts and optionally summarizes them. Supports multiple transcript formats (text, JSON, SRT, WebVTT), multi-language retrieval, and flexible YouTube URL parsing.