Recognition of Chinese Sign Language Based on Orientation Histogram
Huanqing Feng · Jisuanji fangzhen · 2009
Traditional recognition of Chinese sign language mostly uses static feature vectors.This paper proposes an algorithm that extracts efficient feature vectors to recognize a hand gesture for sign language.The proposed algorithm recognizes hand gesture based on visual information without using any special gesture glove.To recognize hand gesture,the proposed method is divided into three steps.First,by using an edge-based hand area search algorithm,a hand block is found and segmented efficiently from the monochrome input images.Secondly,if hand area is successfully extracted,the feature vectors representing the hand shape are analyzed applying orientation histogram scheme.Also,the feature vectors of moving hand is obtained by motion estimation.Lastly,the hand gesture is recognized by feature vectors of hand's shape and movements.The proposed algorithm can not only segment the hand area,but also extract the feature vectors from the gray scaled motion images representing language words.