A statistical feature based decision tree approach for hand gesture recognition

Abstract

A lot of work has been done in the field of sign language recognition all over the world. The main focus of such work is to make the life of vocally impaired people more comfortable. It also bridges the communication gap between the normal and the abnormal people. The deaf and dumb people need not only learn the standard sign language but the core issue is that they can communicate with the normal people of society. It is also not possible for all the normal people that they learn the sign language to understand whatever is said through gestures. So the communicational gap still stays there even after teaching deaf and dumb people with sign language. In this paper, an approach has been presented in which statistical features are extracted from the hand signs and are then fed to the decision tree for the recognition of the hand gestures. In this research the English alphabet gestures data set has been used and the recognized hand gestures are then represented as both the alphabetical and voice forms. This would help the impaired people to communicate with normal people in the way that they can understand.