Sign Language Detection using Deep Learning

This research study presents an innovative approach combining deep learning and Mediapipe to enhance real-time sign language interpretation, crucial for aiding the hard of hearing. By employing Convolutional Neural Networks and Recurrent Neural Networks trained on huge sign language datasets, the system accurately recognizes and interprets various signs. Mediapipe's advanced hand and pose detection capabilities are integrated to track landmarks and gestures precisely in real time. The system processes live video from a webcam, extracts key features using Mediapipe, and classifies gestures with the deep learning models. Designed for diverse gestures and adaptable to different lighting and backgrounds, it ensures low-latency processing for real-time use. Rigorous testing confirms its accuracy and efficiency, demonstrating its potential to enhance accessibility and inclusivity for the hearing impaired.