A phoneme based sign language recognition system using skin color segmentation

Abstract

A sign language is a language which, instead of acoustically conveyed sound patterns, uses visually transmitted sign patterns. Sign languages are commonly developed for deaf communities, which can include interpreters, friends and families of deaf people as well as people who are deaf or hard of hearing themselves. Developing a sign language recognition system will help the hearing impaired to communicate more fluently with the normal people. This paper presents a simple sign language recognition system that has been developed using skin color segmentation and Artificial Neural Network. The moment invariants features extracted from the right and left hand gesture images are used to develop a network model. The system has been implemented and tested for its validity. Experimental results show that the average recognition rate is 92.85%.