A simple sign language recognition system using affine moment blur invariant features

Abstract

Sign language recognition is one of the most promising sub-fields in gesture recognition research. Effective sign language recognition would grant the deaf and hard-of-hearing expanded tools for communicating with both other people and machines. Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. 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 Affine Moment Blur invariants extracted from the right and left hand gesture images are used as feature vector to develop a network model. The system has been implemented and tested for its validity. Experimental results show that the recognition rate is 97.19%.