ocr-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ocr-mcp OCR this scanned invoice and show me the text with confidence scores."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 | ✅ local | per-word | Needs |
ABBYY FineReader 16 | local FineReader Regular CLI ( | ✅ 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 |
| Which engines are usable on this machine + status. Call first. |
| OCR one image. |
| OCR a PDF. |
| OCR many images (glob or JSON list). |
| Run all engines, compare agreement + consensus. |
| 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.exeto 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/WEREvaluation 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 CERSecurity
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 toolsbatch_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
Scored across 6 tools
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.
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.
Six tools is well-scoped for an OCR server, covering essential operations without bloat. Each tool earns its place.
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.
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