Planar shape classification using hidden Markov model

A planar shape-recognition approach is presented which is based on hidden Markov models and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments to make classification at a finer level. The algorithm can tolerate much shape contour perturbation, and a moderate amount of occlusion. The overall classification scheme is independent of shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained all over again when a new class of shapes is added