A Comparative Analysis of ISLRS Using CNN and ViT

Abstract

Indian Sign Language Recognition System (ISLRS) aims at recognizing and interpreting the hand gestures and movements in Indian Sign Language (ISL), in order to facilitate smooth communication between the hearing-impaired individuals and the normal population. This research aims at comparing ISLR System using a custom convolutional neural network (CNN) architecture as well as Vision Transformer (ViT). From the ISL alphabet dataset consisting of 36 classes, 26 classes corresponding to the English alphabets are considered in this analysis. The analysis showed that for the dataset, ViT outperforms CNN in terms of performance metrics considered.