On-line handwriting recognition using continuous parameter hidden Markov models

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Abstract

The problem of the automatic recognition of handwritten text is addressed. The text to be recognized is captured online and the temporal sequence of the data is presented. The approach is based on a left-to-right hidden markov model (HMM) for each character that models the dynamics of the written script. A mixture of Gaussian distributions is used to represent the output probabilities at each arc of the HMM. Several strategies for reestimating the model parameters are discussed. Experiments show that this approach results in significant decreases in error rate for the recognition of discretely written characters compared with elastic matching techniques. The HMM outperforms the elastic matching technique for both writer-dependent and writer-independent recognition tasks.>

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