Evaluation of HMM training algorithms for letter hand gesture recognition
The paper introduces an application using computer vision for letter hand gesture recognition. A digital camera records a video stream of hand gestures. The hand is automatically segmented, the position of the hand centroid is calculated in each frame, and a trajectory of the hand is determined. After smoothing the trajectory, a sequence of angles of motion along the trajectory is calculated and quantized to form a discrete observation sequence. Hidden Markov models (HMMs) are used to recognize the letters. Baum Welch and Viterbi path counting algorithms are applied for training the HMMs. Our system recognizes all 26 letters from A to Z and the database contains 30 example videos of each letter gesture. We achieve an average recognition rate of about 90 percent. A motivation for the development of this system is to provide an alternate text input mechanism for camera enabled handheld devices, such as video mobile phones and PDAs.