HMM-based handwritten word recognition: on the optimization of the number of states, training iterations and Gaussian components
In off-line handwriting recognition, classifiers based on hidden Markov models (HMMs) have become very popular. However, while there exist well-established training algorithms which optimize the transition and output probabilities of a given HMM architecture, the architecture itself, and in particular the number of states, must be chosen by hand. Also the number of training iterations and the output distributions need to be defined by the system designer. In this paper we examine several optimization strategies for an HMM classifier that works with continuous feature values. The proposed optimization strategies are evaluated in the context of a handwritten word recognition task.
handwritten word recognition hidden markov model (hmm) training strategy state number optimization gaussian mixture models