We propose a method to construct a Hidden Markov Model (HMM) for sign language recognition with a topology which is suitable for a variety of hand motions. First, candidate HMMs are generated from sub-motions extracted from training samples. If we have many and various samples of motions, an optimal HMM can be selected from candidates by the maximum likelihood (ML) method. However, it is diffic...
