Published Paper Details:

Communication represents a fundamental human need, yet individuals who are deaf and mute often face significant barriers that can severely limit their access to essential services, educational opportunities, and full social interactions. Traditional sign language interpretation, while invaluable, is frequently unavailable on demand, creating moments of isolation and misunderstanding. This project directly addresses this critical challenge by developing an intelligent translator system that leverages the power of computer vision and deep learning. The primary aim is to accurately recognize dynamic sign language gestures from real-time video frames and seamlessly translate them into both written text and vocalized speech. By integrating a robust deep learning model with real-time video processing and a text-to-speech (TTS) module, this system seeks to bridge existing communication gaps, thereby fostering greater inclusivity and independence for the deaf and mute community. The proposed methodology involves comprehensive data collection from publicly available sign language datasets, meticulous preprocessing, training of a Convolutional Neural Network (CNN) using transfer learning, and deployment via a user-friendly Flask API and/or desktop application. The system's performance will be rigorously evaluated based on accuracy, responsiveness, and usability metrics to ensure its effectiveness in practical, real-world scenarios.