abbyy-finereader-ocr-mcp — Multi-Engine OCR MCP Server

A Model Context Protocol server that gives AI assistants (Claude Code, Codex, Cursor, …) OCR with first-class accuracy handling and evaluation. It wraps three engines behind one interface and can score and compare them:

Engine	Backend	Local?	Confidence	Notes
RapidOCR (default)	PaddleOCR models on onnxruntime	✅ fully local/headless	per-line	No GPU/torch needed; great default
Tesseract	Google Tesseract via pytesseract	✅ local	per-word	Needs tesseract.exe on PATH
ABBYY FineReader 16	local FineReader Regular CLI (/send Clipboard)	✅ local	—	Best accuracy; GUI flashes, 1 doc at a time. Headless file output needs ABBYY's paid Extended CLI

Why multi-engine? No single OCR engine wins on every document. This server lets the model run several, compare their agreement, and score them against ground truth (CER/WER) — so you can pick the right engine per job instead of guessing.

Features

• 📄 OCR images and PDFs (PDFs rasterized via PyMuPDF, per-page OCR).
• 🎯 Confidence scores per line/word, with low-confidence flagging.
• ⚖️ compare_engines — run every available engine on one document and report pairwise agreement + a consensus pick (no ground truth required).
• 📏 evaluate_accuracy — CER / WER, char/word accuracy %, and edit breakdown (substitutions/deletions/insertions) against a ground-truth text file.
• 🧹 Optional preprocessing (grayscale / denoise / deskew via OpenCV).
• 🧱 Fails soft: an unavailable engine is reported, never crashes the server.

Tools

Tool	Description
list_engines()	Which engines are usable on this machine + status. Call first.
ocr_image(path, engine="auto", lang="en", preprocess=False)	OCR one image.
ocr_pdf(path, engine="auto", lang="en", pages="all", dpi=300)	OCR a PDF.
batch_ocr(paths_or_glob, engine="auto", lang="en")	OCR many images (glob or JSON list).
compare_engines(path, lang="en")	Run all engines, compare agreement + consensus.
evaluate_accuracy(ground_truth_path, ocr_text="" | ocr_path="", engine, lang)	CER/WER vs ground truth.

engine ∈ auto (=RapidOCR) · rapidocr · tesseract · finereader. lang is an ISO-639-1 code (en, de, fr, ro, zh, …), mapped per engine.

Requirements

• Python ≥ 3.12 (3.12 recommended — all wheels mature; 3.14 also works for the core RapidOCR path but OpenCV/PyMuPDF wheels may lag).
• Tesseract (optional): install Tesseract-OCR and add tesseract.exe to PATH for that engine.
• ABBYY FineReader 16 (optional): a local install enables the FineReader engine (Regular-CLI clipboard mode). Headless file output requires ABBYY's Extended CLI license.

Install

git clone https://github.com/Prekzursil/abbyy-finereader-ocr-mcp
cd abbyy-finereader-ocr-mcp
uv venv --python 3.12
uv pip install -e .
# (first OCR call downloads the small RapidOCR ONNX models, ~?? MB, cached locally)

Configure

Claude Code

claude mcp add ocr -s user -- "/abs/path/abbyy-finereader-ocr-mcp/.venv/Scripts/python.exe" "/abs/path/abbyy-finereader-ocr-mcp/index.py"

Codex (~/.codex/config.toml)

[mcp_servers.ocr]
command = "D:\\path\\abbyy-finereader-ocr-mcp\\.venv\\Scripts\\python.exe"
args = ["D:\\path\\abbyy-finereader-ocr-mcp\\index.py"]
startup_timeout_sec = 60
tool_timeout_sec = 300

[mcp_servers.ocr.env]
PYTHONUTF8 = "1"
PYTHONUNBUFFERED = "1"

Generic MCP client (mcp.json)

{
  "mcpServers": {
    "ocr": { "command": "/abs/path/.venv/bin/python", "args": ["/abs/path/index.py"] }
  }
}

Usage examples

> OCR this scan and tell me how confident you are.
  → ocr_image("C:/scans/invoice.png")  → text + mean_confidence + low-confidence lines

> Which engine reads this receipt best?
  → compare_engines("C:/scans/receipt.jpg")  → per-engine text + agreement + consensus

> How accurate is RapidOCR on this page vs my transcript?
  → evaluate_accuracy("truth.txt", ocr_path="page.png", engine="rapidocr") → CER/WER

Evaluation methodology

evaluate_accuracy uses jiwer for CER (character error rate) and WER (word error rate). Lower is better; char_accuracy_pct = (1 − CER)·100. Keep ground-truth .txt files next to your test images to track engine accuracy over time. compare_engines is the no-ground-truth fallback: it reports how much the engines agree and which one is the consensus.

Development

uv pip install -e ".[test]"
pytest        # renders known text → OCR → asserts recovery + low CER

Security

This server reads any file path the MCP client gives it — i.e. any file readable by the server process. There is no sandbox by default. Run it only with a trusted MCP client, and be aware that an LLM driving the tools could be prompted to read arbitrary local files.

For defense-in-depth, set OCR_MCP_ALLOWED_DIRS (an os.pathsep-separated list of directories) to restrict all tools to files under those roots:

[mcp_servers.ocr.env]
OCR_MCP_ALLOWED_DIRS = "D:\\scans;D:\\documents"

Also note: batch_ocr with a recursive glob (**/*.png) can match very large file sets — scope your globs. The FineReader engine shells out to the local FineReaderOCR.exe (list-form args, no shell) and reads the OS clipboard.

License

MIT — see LICENSE.

Acknowledgements

RapidOCR · Tesseract · ABBYY FineReader · jiwer · PyMuPDF · MCP