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The handwritten script recognition problem is modeled in the framework of the hidden Markov model. For English text, which is the focus of the present research, the states can be identified with the letters of the alphabet, and the optimum symbols can be generated. In order to do so, a quantitative definition of symbols, in terms of features, is required. Fourteen features (some old, some new) are proposed for this task. Using the existing statistical knowledge about the English language, the calculation of the model parameters is immensely simplified. Once the model is established, the Viterbi algorithm is proposed to recognize the single best optimal state sequence, i.e. sequence of letters comprising the word. The modification of the recognition algorithm to accommodate context information is also discussed. Some experimental results are provided indicating the success of the new scheme.>
Kundu et al. (Mon,) studied this question.