Building a Real-Time Screen Reader on macOS That Actually Works
The author details their journey in building a real-time screen reader for macOS that can detect UI panel boundaries, extract text via OCR, and render a transparent overlay - all running locally without cloud APIs or network round-trips.
Why it matters
This article provides valuable insights and lessons learned for developers trying to build real-time, local screen reading and annotation tools on macOS with Apple Silicon.
Key Points
- 1Explored multiple vision models like Florence-2, Ferret-UI, and Qwen2.5-VL, but faced issues with compatibility, accuracy, and performance on Apple Silicon
- 2Tried traditional computer vision approaches like pixel-edge detection, but they were fragile and failed for UIs with similar background colors
- 3Accessibility API (AX API) on macOS could not access content inside web browsers, limiting its usefulness
- 4Optimized the architecture to avoid spawning a new Python process for each inference, improving performance and reliability
Details
The author needed to build a real-time screen reader for macOS that could detect UI panel boundaries, extract text via OCR, and render a transparent overlay - all running locally without cloud APIs or network round-trips. They explored various vision models like Florence-2, Ferret-UI, and Qwen2.5-VL, but faced issues with compatibility, accuracy, and performance on Apple Silicon. Traditional computer vision approaches like pixel-edge detection were fragile and failed for UIs with similar background colors. The macOS Accessibility API (AX API) could not access content inside web browsers, limiting its usefulness. The author eventually optimized the architecture to avoid spawning a new Python process for each inference, improving performance and reliability.
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