ASLapp

Inspiration

I was inspired by the idea of making communication easier for people who use American Sign Language. I wanted to build something that could help bridge the gap between the deaf community and others through technology.

What it does

This app uses a camera to recognize ASL hand gestures and instantly turns them into letters on the screen. It helps people communicate by translating signs into text in real time.

How we built it

I used computer vision tools to detect hand landmarks and machine learning to train a model on thousands of images of ASL signs. Then, I connected it to a program that shows the detected letter and outlines the hand in the video.

Challenges we ran into

One major challenge was getting the model to recognize signs accurately when the lighting or hand position wasn’t perfect. It also took a lot of time to collect and process training data for each letter.

Accomplishments that we're proud of

I’m proud that I built a working system that can recognize ASL signs with good accuracy. It feels rewarding to see a project like this actually function in real-time after all the effort I put into it.

What we learned

I learned how to use machine learning to classify gestures, how to work with computer vision libraries like MediaPipe, and how important good data is for training an accurate model.

What's next for ASLapp

I plan to expand the app so it can detect full words or sentences in ASL, not just letters. I also want to make it more user-friendly and mobile-compatible, so it can reach more people.