HMM topology optimization for handwriting recognition


This paper addresses the problem of Hidden Markov Model (HMM) topology estimation in the context of on-line handwriting recognition. HMMs have been widely used in applications related to speech and handwriting recognition with great success. One major drawback with these approaches, however, is that the techniques that they use for estimating the topology of the models (number of states, connectivity between the states and the number of Gaussians), are usually heuristically-derived, without optimal certainty. This paper addresses this problem, by comparing a couple of commonly-used heuristically-derived methods to an approach that uses Bayesian Information Criterion (BIC) for computing the optimal topology. Experimental results on discretely-written letters show that using BIC gives comparable results to using heuristic approaches with a model that has nearly 10% fewer parameters.


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