What Inspired Us
Many people face communication barriers because others don’t know sign language. We wanted to use AI to help bridge this gap and make communication more accessible.
What It Does
Sign Language Interpreter uses a camera to recognize signs and convert them into text and speech in real time.
Sign → Text → Voice
How We Built It
We built a web-based AI application using computer vision and machine learning to detect hand movements and signs through a camera and provide instant text and audio output.
Challenges We Ran Into
We faced challenges with accuracy, lighting, backgrounds, camera angles, hand positions, and processing speed. Making recognition fast and reliable required continuous testing and improvement.
Accomplishments We’re Proud Of
We created a working real-time prototype that recognizes signs and converts them into text and voice. Being web-based also makes it easy to access without special hardware.
What We Learned
We learned that a good AI application needs more than a trained model. Data quality, accuracy, speed, accessibility, and user experience all matter.
What’s Next
We plan to improve accuracy, speed, and vocabulary, support full sentences and different environments, and eventually enable two-way communication across multiple sign languages.
Our vision: Communication for everyone, one sign at a time.
Built With
- bolt
- github
- replit
