Sign Language Detection

"Sign Language Detection" is a real-time system developed to bridge the communication gap between the deaf-mute community and the wider population by converting sign language gestures into text. The project leverages MediaPipe for accurate and efficient hand gesture tracking and integrates it with a lightweight TensorFlow model trained on datasets of Indian Sign Language (ISL) and International Sign Language (ASL) sourced from Kaggle. The system takes video input, detects and interprets hand gestures frame by frame, and translates them into meaningful text in real time. The frontend is built using HTML and CSS, with a backend powered by Flask for API integration and MongoDB for managing gesture data and user records. Designed to run efficiently even on low-resource systems, this project provides an accessible and scalable solution for enhancing communication for individuals with hearing and speech impairments.