Signet: A Deep Learning based Indian Sign Language Recognition System

Abstract

Sign language is the primary mode of communication between hearing and vocally impaired population. The government of India has enacted the Rights of Persons with Disabilities Act 2016 (RPwD Act 2016). This act recognizes Indian Sign Language (ISL) as an important communication medium for communicating with hearing impaired people. This also insists the need for sign language interpreters in all Government organizations and public sector undertakings in order to abide RPwD Act 2016. This can avoid their isolation from the rest of society to a great extent. In this work, we propose a signer independent deep learning based methodology for building an Indian Sign Language (ISL) static alphabet recognition system. Here, we review various existing methods in sign language recognition and implement a Convolutional Neural Network (CNN) architecture for ISL static alphabet recognition from the binary silhouette of signer hand region. We also discuss in detail, the dataset used along with the training phase and testing phase of CNN. The proposed method was successfully implemented with an accuracy of 98.64% which is better than most of the currently existing methods.