Indian Sign Language Recognition System

This paper presents an Indian Sign Language (ISL) recognition system, leveraging advanced deep learning techniques to address the communication barriers faced by the hearing-impaired community. The system is designed to recognize and interpret ISL gestures into corresponding text or speech, facilitating effective interaction between hearing-impaired individuals and the broader population. Our approach involves a Convolutional Neural Network (CNN) architecture, specifically ResNet-50, trained on the ISL dataset. Key features include image preprocessing, data augmentation, and real-time gesture recognition. Experimental results demonstrate the system’s high accuracy and robustness, showcasing its potential for practical deployment in various real-world applications. This work contributes significantly to the field of assistive technology, aiming to enhance the inclusivity and accessibility of communication for the hearing-impaired community in India.