Authors: Professor Dr. Rachna Chavan, Ashish Singh
Abstract: Individuals with hearing and speech impairments rely on Indian Sign Language (ISL) for communication. Despite its importance, ISL lacks broad technological integration, limiting accessibility. This paper presents a vision-based recognition model built using Convolutional Neural Networks (CNNs) to classify static ISL gestures. The system undergoes preprocessing, augmentation, training, and real-time classification. A custom dataset was collected to ensure diversity in hand gestures and backgrounds. Our trained model achieved a classification accuracy of 96.4%, showing its capability to assist in inclusive communication tools.
