Recognition of handwritten word: First and second order hidden Markov model based approach

Abstract

In this work, handwritten word recognition problem is modeled in the framework of hidden Markov model (HMM). The states of HMM are identified with the letters of the alphabet. The optimum symbols are then generated by experimental study using fifteen different features. Both the first and second order HMM are used for the recognition task. Using the existing statistical knowledge of English, the calculation scheme for model probabilities are immensely simplified. Once the model is established, Viterbi algorithm is used to recognize the sequence of letters consisting the word. Very high recognition accuracy is obtained with the new scheme.