REAL-TIME HAND GESTURE DETECTION FOR SIGN LANGUAGE RECOGNAITION USING PYTHON

Abstract

Effective communication is vital in daily life, but individuals with hearing or speech impairments often face challenges due to limited understanding of sign language. This project presents a realtime hand gesture detection system for sign language recognition using Python, aiming to bridge this communication gap. The system captures live video input via a camera and uses computer vision techniques to detect and track hand movements. Hand landmarks are extracted using MediaPipe, and machine learning models classify gestures corresponding to specific sign language symbols. Recognized gestures are then converted into text or speech output, providing an interactive and accessible interface for users. By leveraging OpenCV, real-time image processing, and AI-based gesture recognition, the system enables accurate and responsive translation of sign language, promoting inclusivity and social integration. This solution demonstrates how real-time computer vision and AI can enhance communication for individuals with disabilities.