Skip to main content
Glama

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.

Related MCP server: Local Documents MCP 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

Available Tools

6 tools
batch_ocrA

OCR many images. paths_or_glob is a glob (e.g. 'C:/scans/*.png') or a JSON list of absolute paths. Returns JSON: {count, results:[...]}.

Inputs: paths_or_glob (required), engine, lang. Returns: result

compare_enginesA

Run ALL available engines on one image and compare them — the core accuracy tool when you have no ground truth.

Returns JSON: per-engine {text, mean_confidence, ok}, plus pairwise text similarity, average agreement, and a 'consensus_engine' (the one whose output best agrees with the others).

Inputs: path (required), lang. Returns: result

evaluate_accuracyA

Score OCR output against a ground-truth text file (CER/WER).

Provide EITHER ocr_text (already-extracted text) OR ocr_path (an image/PDF to OCR now with engine). Compares against the UTF-8 text at ground_truth_path.

Returns JSON: {cer, wer, char_accuracy_pct, word_accuracy_pct, substitutions, deletions, insertions, hits}.

Inputs: ground_truth_path (required), ocr_text, ocr_path, engine, +1 more. Returns: result

list_enginesA

List OCR engines and whether each is currently usable on this machine.

Returns JSON: for each engine -> {available, status}. Call this first to see which engines compare_engines will actually run.

Inputs: no named parameters. Returns: result

ocr_imageA

OCR a single image file (PNG/JPG/TIFF/BMP).

Args: path: absolute path to the image. engine: 'auto' (RapidOCR), 'rapidocr', 'tesseract', or 'finereader'. lang: ISO 639-1 code ('en','de','fr','ro',...). Mapped per-engine. preprocess: if true, apply grayscale/denoise/deskew first (needs opencv).

Returns JSON: {engine, ok, text, mean_confidence, line_count, low_confidence_count, lines:[{text,confidence,bbox}], warnings}.

Inputs: path (required), engine, lang, preprocess. Returns: result

ocr_pdfA

OCR a PDF by rasterizing pages (PyMuPDF) then running an engine per page.

Args: path: absolute path to the PDF. engine/lang: see ocr_image. pages: 'all' or a range like '1-3,5'. dpi: rasterization DPI (default 300; higher = slower, more accurate).

Returns JSON: {page_count, pages:[{page, ...ocr_image result...}], full_text}.

Inputs: path (required), engine, lang, pages, +1 more. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.1/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool serves a clearly distinct purpose: listing engines, OCR on single image, PDF, batch, cross-engine comparison, and accuracy evaluation. There is no overlap or ambiguity.

    Naming Consistency4/5

    Tool names follow a verb_noun pattern in snake_case, though some use 'ocr' as a verb prefix (ocr_image, ocr_pdf) while others use descriptive verbs (list_engines, compare_engines, evaluate_accuracy). This is mostly consistent and readable.

    Tool Count5/5

    Six tools is well-scoped for an OCR server, covering essential operations without bloat. Each tool earns its place.

    Completeness4/5

    Core OCR workflows are covered: single image, PDF, batch, engine listing, comparison, and accuracy evaluation. Minor gaps (e.g., no tool for engine configuration) but the surface is complete for typical use.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Related MCP Connectors

    Related MCP Servers