Handgestures

Inspiration

Communication through sign language can be challenging when interacting with people who do not understand it. We wanted to develop an AI-powered solution that recognizes hand gestures in real time and converts them into text, making communication more accessible, interactive, and inclusive.

What it does

Handgestures is a real-time hand gesture recognition system that uses computer vision to detect hand movements through a webcam. It recognizes predefined gestures and translates them into readable text, enabling touch-free interaction and assisting basic sign language communication.

How we built it Python OpenCV MediaPipe NumPy Custom Gesture Recognition Logic Webcam for Real-Time Input Challenges we ran into Detecting hand landmarks accurately under different lighting conditions. Reducing false gesture predictions. Handling different hand sizes, orientations, and backgrounds. Maintaining real-time performance while ensuring recognition accuracy. Accomplishments that we're proud of Built a real-time AI-based hand gesture recognition system. Achieved smooth and responsive hand tracking. Successfully converted recognized gestures into readable text. Designed a simple and user-friendly interface. What we learned Building computer vision applications using OpenCV. Implementing hand tracking with MediaPipe. Processing real-time video streams efficiently. Improving gesture recognition through testing and optimization. Understanding the importance of accessibility-focused AI solutions. What's next for Handgestures Support a larger set of sign language gestures. Integrate Text-to-Speech functionality. Add multilingual translation support. Deploy the application as a web and mobile application. Improve recognition accuracy using deep learning models. Enable sentence formation instead of recognizing only individual gestures.