Gesture Recognition for Alphabets from Hand Motion Trajectory Using Hidden Markov Models

This paper describes a method to recognize the alphabets from a single hand motion using Hidden Markov Models (HMM). In our method, gesture recognition for alphabets is based on three main stages; preprocessing, feature extraction and classification. In preprocessing stage, color and depth information are used to detect both hands and face in connection with morphological operation. After the detection of the hand, the tracking will take place in further step in order to determine the motion trajectory so-called gesture path. The second stage, feature extraction enhances the gesture path which gives us a pure path and also determines the orientation between the center of gravity and each point in a pure path. Thereby, the orientation is quantized to give a discrete vector that used as input to HMM. In the final stage, the gesture of alphabets is recognized by using Left-Right Banded model (LRB) in conjunction with Baum-Welch algorithm (BW) for training the parameters of HMM. Therefore, the best path is obtained by Viterbi algorithm using a gesture database. In our experiment, 520 trained gestures are used for training and also 260 tested gestures for testing. Our method recognizes the alphabets from A to Z and achieves an average recognition rate of 92.3%.