K­MEANS CLUSTERING FOR HIDDEN MARKOV MODEL

An unsupervised k­means clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of as much as 8% on a 30,000­word vocabulary, unconstrained­ style, on­line, writer­independent handwriting recognition