Which integrations are available for this server?

Leverages Apple's native Vision Framework to provide high-accuracy OCR capabilities for extracting text from images and PDF documents. Utilizes native macOS system frameworks and tools to provide high-performance OCR services for local PDF and image files.

How do I use OwlOCR MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@OwlOCR MCP extract the text from /Users/username/Desktop/invoice.pdf" 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.

OwlOCR MCP

Python 3.11+ macOS License: MIT

MCP (Model Context Protocol) server for PDF and image OCR on macOS. Supports two backends:

OwlOCR CLI - Higher accuracy (recommended)
Vision Framework - No external dependencies

Features

📄 PDF OCR - Extract text from PDF files page by page with separators
🖼️ Image OCR - Extract text from PNG, JPEG, and other image formats
🌏 Multi-language - Korean + English by default (configurable)
🔄 Dual Backend - Auto-selects OwlOCR if available, falls back to Vision Framework
⚡ Async - Non-blocking execution for MCP clients

Benchmark Results

Tested on a 4-page Korean theological document with Hebrew text:

Metric	Vision Framework	OwlOCR CLI
Time	9.87s	9.30s
Time/Page	2.47s	2.33s
Word Accuracy	85.62%	91.79%
Character Accuracy	94.46%	95.07%

Winner: OwlOCR CLI - Faster and more accurate.

Requirements

macOS (uses Apple Vision Framework / OwlOCR.app)
Python 3.11+
OwlOCR.app (optional, for better accuracy)

Installation

Using uv (recommended)

git clone https://github.com/yourusername/owlocr-mcp.git cd owlocr-mcp uv sync

Using pip

git clone https://github.com/yourusername/owlocr-mcp.git cd owlocr-mcp pip install -e .

MCP Client Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{ "mcpServers": { "owlocr": { "command": "uv", "args": ["run", "--directory", "/path/to/owlocr-mcp", "owlocr-mcp"] } } }

Generic MCP Client

{ "mcpServers": { "owlocr": { "command": "/path/to/owlocr-mcp/.venv/bin/python", "args": ["-m", "owlocr_mcp.server"] } } }

Available Tools

`ocr_pdf_to_text`

Extract text from a PDF file.

Parameters:

Parameter	Type	Default	Description
`pdf_path`	string	required	Absolute path to the PDF file
`pages`	list[int]	null	Page numbers to process (1-based). If null, all pages
`dpi`	int	200	Resolution for rendering. Higher = better quality but slower
`backend`	string	"auto"	`"auto"`, `"owlocr"`, or `"vision"`
`languages`	list[string]	null	Language codes (Vision only). Default: `["ko-KR", "en-US"]`

Example:

Extract text from /Users/me/document.pdf using OwlOCR

Output:

첫 번째 페이지 내용... ===== Page 2 ===== 두 번째 페이지 내용... --- OCR Complete: 2 page(s) processed using OwlOCR CLI ---

`ocr_image_to_text`

Extract text from an image file.

Parameters:

Parameter	Type	Default	Description
`image_path`	string	required	Absolute path to the image file
`backend`	string	"auto"	`"auto"`, `"owlocr"`, or `"vision"`
`languages`	list[string]	null	Language codes (Vision only)

`check_ocr_backends`

Check available OCR backends on the system.

Output:

OCR Backend Status: ✅ Vision Framework: Available (macOS built-in) ✅ OwlOCR CLI: Available (/Applications/OwlOCR.app) Recommendation: Use backend='owlocr' for best accuracy

Backend Selection

Backend	Accuracy	Speed	Requirements
`owlocr`	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	OwlOCR.app installed
`vision`	⭐⭐⭐⭐	⭐⭐⭐⭐	None (macOS built-in)
`auto`	Best available	-	Uses OwlOCR if available

Running the Benchmark

Compare backends on your own PDF:

# Both backends uv run python benchmark.py /path/to/your.pdf # With accuracy comparison (requires ground truth) uv run python benchmark.py /path/to/your.pdf --show-text # Specific backend only uv run python benchmark.py /path/to/your.pdf --method owlocr uv run python benchmark.py /path/to/your.pdf --method vision

Project Structure

owlocr-mcp/ ├── src/owlocr_mcp/ │ ├── __init__.py │ ├── server.py # MCP server with tools │ ├── ocr.py # Vision Framework backend │ ├── ocr_owlocr.py # OwlOCR CLI backend │ └── pdf.py # PDF processing utilities ├── benchmark.py # Performance comparison script ├── pyproject.toml └── README.md

How It Works

OwlOCR Backend

Render PDF pages to PNG using pypdfium2
Copy images to OwlOCR sandbox: ~/Library/Containers/JonLuca-DeCaro.OwlOCR/Data/tmp/
Run CLI: /Applications/OwlOCR.app/Contents/MacOS/OwlOCR --cli --input <file>
Combine results with page separators

Vision Framework Backend

Render PDF pages to PNG using pypdfium2
Load as CIImage via PyObjC
Create VNRecognizeTextRequest with accurate recognition level
Process with VNImageRequestHandler
Sort results by position and combine

Troubleshooting

"OwlOCR.app not found"

Install OwlOCR from owlocr.com or use backend="vision".

File picker dialog appears

This happens when OwlOCR can't access files outside its sandbox. The MCP server handles this by copying files to the sandbox temp directory automatically.

Poor accuracy on specific languages

For Vision Framework, specify languages explicitly:

ocr_pdf_to_text(pdf_path, languages=["ja-JP", "en-US"])

Supported language codes: ko-KR, en-US, ja-JP, zh-Hans, zh-Hant, etc.

License

MIT License - see LICENSE file.

Acknowledgments

OwlOCR by JonLuca DeCaro
MCP Python SDK
Apple Vision Framework

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

How are these scores calculated?

Resources

GitHub Repository

Need Help?

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Related Servers

OwlOCR MCP

OwlOCR MCP

Features

Benchmark Results

Requirements

Installation

Using uv (recommended)

Using pip

MCP Client Configuration

Claude Desktop

Generic MCP Client

Available Tools

`ocr_pdf_to_text`

`ocr_image_to_text`

`check_ocr_backends`

Backend Selection

Running the Benchmark

Project Structure

How It Works

OwlOCR Backend

Vision Framework Backend

Troubleshooting

"OwlOCR.app not found"

File picker dialog appears

Poor accuracy on specific languages

License

Acknowledgments

Resources

New MCP Servers

Latest Blog Posts

MCP directory API