Inspiration
Our main inspiration for SignBridge came from recognizing the everyday communication barriers faced by the hearing and speech-impaired community. While technology has advanced rapidly, accessible tools that seamlessly translate sign language into text in real time are still not widely integrated into everyday software. We wanted to build a practical, easy-to-use solution that leverages modern computer vision to make communication more inclusive and bridge the gap between sign language users and the hearing world using standard web devices.
What it does
SignBridge is an web application designed to bridge the communication gap for hearing and speech-impaired individuals. The system processes hand gestures from a live webcam stream or uploaded video files, detects 3D positional hand landmarks, and translates sign language gestures into textual output in real time.
How we built it
- Frontend: Built with a modern web framework to provide a responsive, user-friendly interface for real-time video capture and text display.
- Computer Vision & AI: Implemented robust hand-tracking models (such as MediaPipe) to accurately capture 3D spatial hand coordinates and landmarks from video frames.
- Processing Pipeline: Designed a frame-processing pipeline to analyze landmark data sequentially and map gesture patterns to corresponding text tokens.
Challenges we ran into
- Real-time Performance: Ensuring low-latency processing for live webcam feeds required optimizing the computer vision pipeline to run smoothly in the browser.
- Lighting and Angle Variations: Managing varying lighting conditions and different hand angles, which can occasionally affect the accuracy of landmark detection.
- Gesture Mapping: Aligning continuous motion gestures into distinct textual outputs without significant lag.
Accomplishments we're proud of
- Successfully building a working prototype that translates sign gestures into real-time text using standard web equipment (a regular webcam).
- Creating a clean, intuitive interface that makes accessibility tech easy to use for anyone.
- Successfully integrating live video stream processing directly within a web environment.
What we learned
- Gained deeper practical experience in computer vision, spatial landmark tracking, and real-time data processing in web applications.
- Understood the importance of optimizing frontend performance when handling resource-intensive video streams and AI models.
- Learned more about designing software centered around accessibility and real-world utility.
What's next
- Multi-Hand Tracking & Recognition: Expand the computer vision pipeline to support concurrent tracking of multiple hands for complex sign language gestures and phrases.
- Text-to-Speech (TTS) Integration: Implement client-side text-to-speech synthesis to convert translated text output into natural voice audio, enabling true two-way communication.
- WebSocket Protocol Integration: Replace HTTP polling mechanisms with persistent WebSockets to drastically reduce latency and optimize real-time frame streaming performance.
- Expanded Vocabulary Database: Train and integrate a more comprehensive lexicon of regional and international sign language dictionaries to improve translation accuracy.
- Offline Edge Processing: Explore lightweight on-device model quantization to minimize server dependency and enhance overall execution speed.
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