A hidden Markov model (HMM) is a doubly stochastic process with an underlying stochastic process that is not observable, i.e., states are hidden, but can only be observed through another set of stochastic processes that produce the observable sequence of symbols. In this work, the handwritten script recognition problem is modeled in the framework of HMM. For English text, which is the focus of the present research, the states are identified with the letters of the alphabet, and the optimum symbols are generated by means of experimental study. Fifteen features (some old, some new) are used for this task. Both the first and second order hidden Markov models are used for the recognition task. Using the existing statistical knowledge of the English language as in Cryptography etc., the calculation scheme of the model parameters are immensely simplified for the first order model. Extending these results and through an exhaustive dictionary search, probabilities of the second order model are calculated. Viterbi algorithm is used to recognize the single best optimal state sequence, i.e., sequence of letters consisting the word. The modification of the recognition algorithm to accommodate context information is also researched. Finally, experimental results for the first and second order models are provided.
