Isolated sign language characters recognition

Abstract

People with normal senses use spoken language to communicate with others. This method cannot be used by those with hearing and speech impaired. These two groups of people will have difficulty when they try to communicate to each other using their own language. Sign language is not easy to learn, as there are various sign languages, and not many tutors are available. This study focuses on the character recognition based on manual alphabet. In general, the characters are divided into letters and numbers. Letters were divided into several groups according to their gestures. Characters recognition was done by comparing the photograph of a character with a gesture dictionary that has been previously developed. The gesture dictionary was created using the normalized Euclidian distance. Character recognition was performed by using the nearest neighbor method and sum of absolute error. Overall, the level of accuracy of the proposed method was 96.36%.