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vision-framework

Implement on-device OCR, face detection, barcode detection, and tracking with Vision and CoreML.

1|1|Updated Apr 11, 2026
Search Tags:#ocr#vision#on-device#image-processing#face-detection#coreml#barcode
One-click install
npx skills add https://github.com/Borisserz/FoodTracker --skill vision-framework-borisserz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vision-framework
Source: https://github.com/Borisserz/FoodTracker/tree/main/.agents/skills/vision-framework
Command: npx skills add https://github.com/Borisserz/FoodTracker --skill vision-framework-borisserz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

On-device computer vision capabilities for OCR, face and barcode detection, segmentation, and tracking, enabling fast, privacy-preserving results without cloud processing.

Core Features & Use Cases

  • OCR for text extraction from images and documents, with language hints and accuracy settings.
  • Face detection, barcode detection, image segmentation, and instance/object tracking for real-time UI effects and data extraction.
  • Use cases: capture IDs, receipts, product labels, or live scanning in apps requiring offline analysis.

Quick Start

Install the Vision framework and run a sample image through OCR, barcode, and face-detection pipelines to observe on-device results

Frequently Asked Questions about vision-framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform on-device OCR text extraction in iOS without cloud processing?▼

On-device OCR text extraction uses the Vision framework's RecognizeTextRequest to analyze images and documents locally. You can apply language hints and accuracy settings to extract text from IDs, receipts, and product labels without relying on cloud processing.

What is on-device computer vision for face and barcode detection in iOS apps?▼

On-device computer vision detects faces, barcodes, and objects using local hardware. It utilizes Vision framework patterns like DetectFaceRectanglesRequest and DetectBarcodesRequest to evaluate live camera feeds and video frames for real-time UI effects and data extraction.

Does the Vision framework support live camera analysis and video frame evaluation on iOS 16?▼

Yes, Vision framework supports live camera analysis and video frame evaluation across iOS 16+. It integrates modern Vision patterns and legacy VNRequest-based flows to provide real-time scanner capabilities with broad compatibility.

Can I use CoreML integration with VisionKit for image segmentation and object tracking?▼

Yes, CoreML integration works with VisionKit for image segmentation and instance tracking. This combination enables real-time UI effects and offline data extraction by running machine learning models directly on the device.

What is the best way to handle legacy VNRequest compatibility for barcode detection in iOS?▼

For legacy compatibility, barcode detection can utilize standard VNRequest-based flows alongside modern DetectBarcodesRequest patterns. This approach ensures broader compatibility across iOS versions while maintaining on-device processing capabilities.

Why do I need on-device image processing instead of cloud-based vision for my iOS app?▼

On-device image processing provides privacy-preserving results with faster response times by eliminating network latency. It enables offline analysis for live scanning, ensuring sensitive data like IDs and receipts never leave the device.

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