Authors: Dr. Harsha R. Vyawahare, Sukhada Shripad Tare, Ashwini Nitin Shingane, Shreya Sunil Shinde, Bhavika Suraj Jain
Abstract: This paper presents a practical and lightweight bidirectional communication system that translates between speech/text and Indian Sign Language (ISL) using machine learning and computer vision techniques. The system supports two modes: Speech-to-ISL and ISL-to-Text/Speech. In Speech Mode, spoken input is converted into text using speech recognition, then mapped to corresponding ISL alphabet images. In Camera Mode, hand gestures are captured using a webcam and classified using a Convolutional Neural Network (CNN) model to generate text and voice output. The system is implemented using Streamlit for the user interface, OpenCV for image processing, TensorFlow/Keras for gesture recognition, and pyttsx3 for speech synthesis. The proposed system provides a simple, real-time, and cost-effective solution to improve communication accessibility for the Deaf and Hard-of-Hearing (DHH) community
DOI: https://doi.org/10.5281/zenodo.19484295
