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sandraschi/ocr-mcp

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Repository Install

FastMCP server providing advanced OCR capabilities with current state-of-the-art models (DeepSeek-OCR, Florence-2, DOTS.

概览

A with a streamlined dashboard (drag-and-drop or scanner, pick an engine, click one button) and a for agentic IDEsClaude, Cursor, Windsurfso agents can run OCR, preprocessing, and workflows as tools. Same 14 engines, WIA scanner (Windows), and pipelines; one repo. ocr, mcp, fastmcp, document-processing, scanner, wia, pdf, computer-vision, model-context-protocol, llm - React (web_sota/) + FastAPI (backend/app.py): upload or scan, pick engine, get text/PDF/JSON. Ports (Vite) and (API). In-app (/help) documents the web UI, the MCP server, and OCR backends. - FastMCP 3.1 stdio: tools for OCR, preprocessing, scanner, workflows. (http://127.0.0.1:11434/v1, model llama3.2) no cloud API key. Set to use the host IDEs LLM instead. Mistral OCR uses when you call that backend. See AI_FEATURES.md. 14 backends (Unlimited-OCR, PaddleOCR-VL-1.5, Nemotron VL 8B, DeepSeek-OCR-2, MinerU2.

README

OCR-MCP

Complete AI OCR webapp and MCP server. A web app with a streamlined dashboard (drag-and-drop or scanner, pick an engine, click one button) and a FastMCP 3.1 MCP server for agentic IDEsClaude, Cursor, Windsurfso agents can run OCR, preprocessing, and workflows as tools. Same 14 engines, WIA scanner (Windows), and pipelines; one repo.

Topics: ocr, mcp, fastmcp, document-processing, scanner, wia, pdf, computer-vision, model-context-protocol, llm

What it does

  • Web app React (web_sota/) + FastAPI (backend/app.py): upload or scan, pick engine, get text/PDF/JSON. Ports 10858 (Vite) and 10859 (API). In-app Help (/help) documents the web UI, the MCP server, and OCR backends.
  • MCP server FastMCP 3.1 stdio: tools for OCR, preprocessing, scanner, workflows. Sampling defaults to local Ollama (http://127.0.0.1:11434/v1, model llama3.2) no cloud API key. Set OCR_SAMPLING_USE_CLIENT_LLM=1 to use the host IDEs LLM instead. Mistral OCR uses MISTRAL_API_KEY when you call that backend. See AI_FEATURES.md.

Features: 14 backends (Unlimited-OCR, PaddleOCR-VL-1.5, Nemotron VL 8B, DeepSeek-OCR-2, MinerU2.5-Pro, Mistral OCR) Auto backend selection Preprocessing (deskew, enhance, crop) Layout & table extraction Form detection & reconstruction (checkbox, text field, radio, signature detection → fillable ODT via libreoffice-mcp) Quality assessment WIA scanner Auto-Scan watcher (detect documents on flatbed, auto-OCR) Batch & pipelines Multi-format export

Docs

Doc Description
Install Install, run MCP, Web UI (start.ps1, ports 10858/10859), PyYAML notes, client config
Backend deps Web FastAPI backend: same venv as ocr-mcp, pyproject.toml, PyTorch, OCR_AUTO_INSTALL_DEPS
Technical Architecture, tools, config, development, packaging
OCR models Engines, capabilities, hardware (see also AI_MODELS.md)
Backend requirements Per-model pip packages, system deps, env/config
MCP toolset matrix Portmanteau tools, operation status, corpus v0
AI features Sampling, SEP-1577, agentic workflows, prompts
Webapp redesign July 2026 redesign: dashboard-first workflow, removed legacy frontend, 5-page sidebar
Book scanning Home book scanning guide: V-cradle, CZUR, auto-scan integration
Book pipeline Spine-to-EPUB pipeline plan: cut, scan, OCR, chapter detect, EPUB, Calibre
In-app Help Source for /help: webapp vs MCP vs backends (mirrors INSTALL / TECHNICAL)
SOTA Compliance Verified SOTA v12.0 Architecture

Also: JUSTFILE.md (just recipes) OCR-MCP_MASTER_PLAN.md (roadmap) tests/README.md (testing)

Quick Start

git clone https://github.com/sandraschi/ocr-mcp
cd ocr-mcp
just

This opens an interactive dashboard showing all available commands. Run just bootstrap to install dependencies, then just serve or just dev to start.

Manual Setup

If you don’t have just installed:

🛡️ Industrial Quality Stack

This project adheres to SOTA 14.1 industrial standards for high-fidelity agentic orchestration:

  • Python (Core): Ruff for linting and formatting. Zero-tolerance for print statements in core handlers (T201).
  • Webapp (UI): Biome for sub-millisecond linting. Strict noConsoleLog enforcement.
  • Protocol Compliance: Hardened stdout/stderr isolation to ensure crash-resistant JSON-RPC communication.
  • Automation: Justfile recipes for all fleet operations (just lint, just fix, just dev).
  • Security: Automated audits via bandit and safety.

License

MIT see LICENSE.

View this README on GitHub

安装

This server does not publish a one-line install command.

Open the repository installation guide