dsh-macos-vision-ocr

English | 简体中文

Offline OCR for DeepSeek Harness, powered by Apple's macOS Vision framework. The plugin adds an ocr_image tool that lets any text model extract text from screenshots, scans, and document images without an API key or network request.

Features

• Runs locally with VNRecognizeTextRequest at accurate recognition level.
• Supports PNG, JPEG, WebP, GIF, TIFF, BMP, HEIC, and HEIF.
• Accepts BCP-47 recognition languages per call.
• Compiles its small embedded Swift helper on first use, then reuses a content-addressed cache.
• Returns bounded output and reports whether text was truncated.
• Uses fixed subprocess argument vectors; image paths are never interpolated into a shell command.

Requirements

• macOS 13 or later.
• Xcode Command Line Tools with swiftc available on PATH.
• DeepSeek Harness 0.1.0-rc.5 or a compatible developer-preview build.

This plugin intentionally fails on Linux and Windows because Apple Vision is not available there.

Install

Install directly from GitHub into any profile that should expose OCR:

dsh plugin --profile web add github:leozou320-ai/dsh-macos-vision-ocr

Restart the profile after installation. To remove it:

dsh plugin --profile web remove dsh-macos-vision-ocr

Usage

Ask the agent to read an image, or call the tool explicitly:

{
  "file_path": "./scan.png",
  "languages": ["zh-Hans", "en-US"]
}

The result contains the canonical image path, recognized text, selected languages, and a truncated flag.

Configuration

Edit this package's row in a later Harness patch layer when you need different defaults:

- id: dsh-macos-vision-ocr
  config:
    cacheDir: /absolute/path/to/cache
    languages: [en-US]
    maxOutputBytes: 2000000

Permissions, privacy, and security

• OCR is local and the plugin makes no network requests.
• Image paths are checked through Harness's filesystem service before the native helper runs, so the active filesystem policy still controls access.
• The plugin runs swiftc once and then executes the cached native helper through Harness's subprocess service.
• Recognized text becomes tool output and therefore enters the current session transcript and model context. Do not OCR material you would not send to the configured model provider.
• Review third-party plugin source before installation and pin a commit for sensitive deployments.

Known limitations

• Text recognition is not general visual understanding; it does not identify objects, faces, or scenes.
• Reading order is a geometric approximation and can be imperfect for multi-column or highly stylized layouts.
• The first call is slower because the Swift helper must compile.
• Handwriting quality depends on language, image quality, and the macOS Vision version.

Development

node --check host.mjs
node --test
npm pack --dry-run

For a local install test:

dsh plugin --profile web add ./path/to/dsh-macos-vision-ocr

License

MIT