Basic Investigation for Improvement of Sign Language Recognition Using Classification Scheme
Sign language is a commonly used communication method for hearing impaired or speech impaired people. However, it is quite difficult to learn sign language. If automatic translation for sign language can be realized, it becomes very meaningful and convenient not only for impaired people but also physically unimpaired people. The cause of the difficulty in automatic translation is that there are so many variations in sign language motions, which degrades recognition performance. This paper presents a recognition method for maintaining the recognition performance for many sign language motions. A scheme is introduced to classification using a decision tree, which can decrease the number of words to be recognized at a time by dividing them into groups. The used hand, the characteristics of hand motion and the relative position between hands and face have been considered in creating the decision tree. It is confirmed by experiments that the recognition success rate increased from 41 and 59 to 59 and 82 respectively, for a basic 17 words of sign language with four sign language operators.
HCI