SignSpeak
Inspiration
Communication should be accessible to everyone, yet many people face barriers when interacting with those who use sign language. We wanted to build a solution that leverages AI and computer vision to make sign language learning and recognition more accessible. Our goal was not only to recognize signs in real time but also to create an engaging platform that helps users learn through practice and interactive challenges.
What it does
SignSpeak is an AI-powered sign language learning and recognition platform.
Users can:
- Learn sign language letters through guided reference materials.
- Practice individual signs with real-time feedback.
- Use their webcam to perform signs and receive instant predictions.
- Play an interactive Hangman-style game powered by gesture recognition.
- Track progress through timed practice sessions and completion feedback.
The platform combines accessibility, education, and gamification into a single experience.
How we built it
Frontend
- React
- TypeScript
- Modern responsive UI design
- Real-time WebSocket integration
Backend
- Python
- FastAPI
- WebSocket communication for live updates
- Game session management
AI & Computer Vision
- Webcam-based hand tracking
- Machine learning gesture recognition model
- Real-time prediction pipeline
- Letter classification for sign language recognition
Features
- Interactive learning guide
- Real-time sign interpreter
- Timed practice mode
- Hangman challenge mode with hints
- Progress indicators and achievement feedback
Challenges we ran into
Building a real-time AI application introduced several challenges:
- Maintaining stable WebSocket communication between the frontend and backend.
- Synchronizing game state updates with live prediction results.
- Preventing duplicate completion events and repeated notifications.
- Managing and deploying large reference image assets correctly.
- Handling browser caching issues during development and testing.
- Designing an interface that remained simple while supporting multiple learning modes.
- Balancing prediction accuracy with responsiveness for a smooth user experience.
Accomplishments that we're proud of
- Successfully built a working real-time sign language recognition system.
- Created an engaging learning experience rather than a simple classifier.
- Implemented multiple learning modes, including practice sessions and challenge-based gameplay.
- Developed a polished and responsive user interface.
- Integrated AI, computer vision, backend services, and frontend technologies into a single platform.
- Delivered a complete end-to-end accessibility-focused application ready for demonstration.
What we learned
Throughout this project, we gained valuable experience in:
- Computer vision and machine learning integration.
- Real-time application architecture using WebSockets.
- FastAPI backend development.
- React and TypeScript frontend engineering.
- State management across distributed systems.
- UI/UX design for educational applications.
- Debugging deployment, asset management, and browser caching issues.
Most importantly, we learned how technology can be used to create meaningful solutions that improve accessibility and communication.
What's next for SignSpeak
We plan to continue expanding SignSpeak by:
- Supporting full words and sentences instead of individual letters.
- Improving model accuracy with larger training datasets.
- Adding support for additional sign languages and regional variants.
- Introducing user accounts and progress tracking.
- Creating more educational games and challenges.
- Expanding to mobile platforms.
- Enhancing accessibility features for broader adoption.
Our vision is to transform SignSpeak into a comprehensive AI-powered platform for learning, practicing, and translating sign language.
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