Computer Vision for Accessibility and Assistive Tech
Computer vision helps machines understand what we see. In accessibility work, it can turn scenes into useful descriptions, actions, and cues. This makes digital and physical spaces more navigable for people with vision, hearing, or mobility differences.
Real-time image description is common for screen readers and smart devices. An app can describe a photo on a social feed, or narrate a busy street as someone walks. Practical uses include generating alt text for images, reading text from signs with OCR, and providing captions for live video. Small examples show how CV can boost independence: a phone reads the menu in a cafe, and a wearable explains a classroom slide to a student.
Key use cases include:
- Real-time alt text for websites to support screen readers
- OCR to read signs, labels, or documents aloud
- Captioning for videos and basic sign language recognition
- Object or scene recognition to support navigation in unfamiliar places
Color and contrast checks help designers ensure readability for colorblind users. Landmark detection, depth cues, and map-like guidance can support mobility aids and navigation. AR glasses or smartphones can describe doors, stairs, or elevators and even read price tags in stores.
Challenges and care matter. Accuracy can vary with lighting, angle, and data quality. Bias and privacy concerns require careful design, opt-in choices, and clear data practices. On-device processing and privacy-by-design approaches help protect users.
Practical steps to start:
- Begin with clear user research and quick tests with diverse participants
- Choose tasks that add real value, such as alt text, captions, or OCR
- Provide safe defaults, easy opt-out, and manual override options
- Favor on-device processing when possible and minimize data collection
- Test with assistive technologies and gather ongoing feedback
Getting started with CV for accessibility means small, meaningful wins. A classroom app that describes slides, a retail tool that reads labels, and a navigation aid that explains scenes—these examples show how thoughtful technology can expand possibilities.
Key Takeaways
- Computer vision powers real-world accessibility by describing, reading, and guiding users in real time.
- Privacy, bias, and user control are essential in design and implementation.
- Start with user needs, keep options simple, and test with diverse readers and users of assistive tech.