Cascaded HMM training algorithm for continuous character recognition

Continuous handwritten recognition, including the recognition of sentences, words and character sequences is a new and important branch in character recognition. One of the key techniques in Continuous Handwritten Character Recognition is how to model every word entry in lexicon and every sentence that made up by the words. Due to the HMM's characteristic of time sequence modeling capability, a Cascaded Hidden Markov Models (Cascaded HMM), which defines model connection probability and state transition probability between HMMs, is proposed in this study. By modifying the Baum-Welch algorithm reestimation formula, Cascaded HMM reestimates connection parameter of character HMMs. The description of Cascaded Baum Welch Algorithm and Cascaded Viterbi Algorithm are given. The cascaded idea of recognizing continuous character is with the strategy of free-segmentation and dynamic programming. Meanwhile, Cascaded HMMs do not model every item listed in lexicon, but combine character models as continuous text model. The cascaded HMM method could accurately describe the shape variability between adjacent characters in handwritten curve. In handwritten English word recognition task, test result shows that the cascaded model is prior to the baseline system. The method offers strong support for continuous recognition technology.