Published Paper Details:

In a world striving for inclusivity, enhancing communication with the hearing-impaired community remains a crucial area of focus. This project aims to develop a deep learning-based system for sign language recognition, improving real-time gesture interpretation to bridge communication gaps effectively. Leveraging advanced computer vision techniques with convolutional neural networks (CNNs) for gesture detection and feature extraction, the system recognizes and translates sign language gestures into text or speech. By integrating OpenCV for efficient image and video processing, and TensorFlow/ Keras for model training and deployment, the system is designed for accuracy, scalability, and real-time performance. A user-friendly interface, built with Streamlit, ensures accessibility for diverse users and provides an essential tool to facilitate interactions between hearing and hearing-impaired individuals across various social and professional contexts.