Hand gesture recognition using orientation histogram

We propose 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 gestures, the proposed method is uses three steps. First, by an edge-based hand area search algorithm, a hand block is found and segmented efficiently from the monochrome input images. Second, if the hand area is successfully extracted, the feature vectors representing the hand shape are analyzed applying orientation histogram scheme. Also, the feature vectors of the moving hand is obtained by motion estimation. In the last step, we recognize hand gesture by feature vectors of the 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 5 sign language words.