Hand Shape Recognition Using the Bending Element of Fingers in Sign Language.

Abstract

In this study, at first, it classifies hand shape of sign language in order to more efficiently recognize the hand shape, and extracting the element of the hand shape, it is confirmed that bending element of each finger is the most important for the sign language. Next, the recognition method which classifies the each finger bending condition into 5 states is proposed, as to give the detailed erratum judgement of what kind of condition the each finger may be for the learner in hand shape of sign language, is possible. In addition, the recognition method by error reverse propagation method of neural network is proposed as hand shape measuring device for the hand shape recognition in order to recognize the hand shape in which there is individual difference using data glove which can measure the bending angle of the joint of the finger. Then, the effectiveness of the technique that carries out and proposes the commercial hand shape recognition experiment is confirmed.