Evaluating a Hidden Markov Model Of Syntax In A Text Recognition System
Recognition of text by whole word shapes generates a set of candidate words for each printed word. A Hidden Markov Model (HMM) of syntax may be used to find the most probable sequence of syntactic tags for a sentence given the sequence of candidate sets. Candidate sets are then reduced by removing all words which are not associated with the chosen tag. We show that the tagging performance of the HMM does not deteriorate despite an increasing proportion of mis-classified words. We also show that using the model significantly reduces the number of candidates.
KeywordsHide Markov Model Viterbi Algorithm Test Sentence Word Image Chain Code
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