Developing a Translation Model to Bridge ASL and Indian Regional Languages Using Neural Networks

In this resarch work, a real-time hand gesture detection system for Ame rican Sign Language (ASL) that enables the deaf-dumb co mmun ity to interact with others by translating sign language into text. The current methods for translating ASL to English primarily use computer vision tools like Media Pipe and OpenPose to recognise hand landma rks and motions. To tackle this issue, we provide a novel convolutional neural network (CNN) architecture that removes the need for manual feature engineering by automatically obtaining discriminative spatial-temporal characteristics from raw video streams. CNNs are more suited to handle the dynamic and highly changeable nature of hand movements because they are very good in simu Itaneously recording spatial and temporal informat ion, unlike LSTM (Long Short-Term Memory) networks, which are frequently emp loyed for sequential data. CNNs are perfect for rea l-t ime applications because they provide more efficient processing and faster inference time. By using multilingual language translators and advanced CNNbased deep learning models, we aim to reduce the communication gaps across India's diverse linguistic minorities. To ensure accurate performance evaluation and successful model learning, the dataset was divided into 70 % for tra inin <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$g$</tex> and 30 % for testing. Compared to tradit ional methods, the proposed approach has a 95% accuracy rate. With potential uses in public services, healthcare, and education, this program encourages assistive technology by offering a practical tool to improve communicat ion between sign language users and the general public. Through encouraging interlanguage contact, the system helps create a society that is more inclusive.