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SignBride AI
Inspiration
Communication is a basic human right, yet millions of people who rely on sign language face daily barriers when interacting with the wider world. We noticed that most sign language translation tools are either expensive, hardware-dependent, or limited in real-time usability. This gap in accessibility inspired us to build SignBridge AI — a solution that uses artificial intelligence to translate sign language from video into live captions and natural voice output, making communication more inclusive and effortless.
What it does
SignBridge AI is an AI-powered system that translates sign language from live video or uploaded recordings into real-time text captions and spoken audio. It detects hand gestures frame-by-frame, identifies sign language gestures, refines them into meaningful sentences using generative AI, and outputs both captions and voice. This allows seamless communication between sign language users and non-signers in environments such as classrooms, hospitals, and public services.
How we built it
We built SignBridge AI as a modular AI pipeline:
Video Input – Accepts live webcam input or uploaded video files.
Hand Detection – Uses MediaPipe Hands to extract hand landmarks from each frame.
Gesture Classification – Processes landmark positions to classify individual sign gestures.
AI Language Refinement – Uses Google Gemini to convert raw gesture outputs into grammatically correct and natural sentences.
Output Generation – Displays live captions and generates voice output using text-to-speech.
User Interface – Built using Streamlit for rapid prototyping and interactive web deployment.
Mathematically, each detected hand is represented as: H={(x1,y1),(x2,y2),…,(x21,y21)} which forms the basis for gesture recognition.
Challenges we ran into
We faced several real-world challenges during development:
Managing dependency conflicts, especially with MediaPipe and Python versions
Handling real-time video processing performance
Dealing with camera access limitations in browser-based environments
Preventing audio runtime conflicts in continuous video loops
Converting raw gesture outputs into meaningful, context-aware sentences
Each challenge required debugging, architectural changes, and careful optimization.
Accomplishments that we're proud of
Successfully built a working end-to-end prototype within hackathon constraints
Achieved real-time captions and voice translation from video input
Integrated computer vision, generative AI, and speech synthesis into one system
Designed a solution that is reproducible, scalable, and demo-stable
Focused on social impact and accessibility, not just technical complexity
What we learned
Through this project, we learned:
Practical application of computer vision and MediaPipe
How to design AI pipelines that combine CV, NLP, and speech
Effective use of Google Gemini for language refinement
Importance of robust error handling and fallback mechanisms
How to build demo-ready AI systems under real-world constraints
What's next for SignBridge AI: Real-Time Sign Language Translation
In the future, we aim to:
Expand support for full sentence-level sign recognition
Add multiple sign languages and regional variations
Enable subtitle (SRT) export for videos
Improve accuracy using larger gesture datasets
Deploy as a mobile-friendly and cloud-scalable application
Our long-term goal is to make SignBridge AI a widely accessible tool that truly bridges communication gaps.
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