CURVELET TRANSFORM AND HMM CLASSIFIER BASED SIGN LANGUAGE RECOGNITION SYSTEM

Abstract

A communication tool in the form of sign language is required for deaf and dump persons as there is no oral communication possible between them. They perform the conversion of sign languages into voice/text. Recently, many algorithms are developed for this purpose. An Indian Sign Language Recognition (ISLR) system is presented in this paper. It uses curvelet transform based entropy features for the recognition, and the transform is applied only to the segmented hand region. Then, the features of each sign of English alphabets are modelled by a classier network called Hidden Markov Models (HMM). The system gives an average accuracy of 82.95% using 3rd level features which can help to reduce the communication gap between deaf-dumb and normal people in the world.