A hybrid large vocabulary handwritten word recognition system using neural networks with hidden Markov models
نویسندگان
چکیده
In this paper we present a hybrid recognition system that integrates hidden Markov models (HMM) with neural networks (NN) in a probabilistic framework. The input data is processed first by a lexicon–driven word recognizer based on HMMs to generate a list of the candidateN–best– scoring word hypotheses as well as the segmentation of such word hypotheses into characters. An NN classifier is used to generate a score for each segmented character and in the end, the scores from the HMM and the NN classifiers are combined to optimize performance. Experimental results show that for an 80,000–word vocabulary, the hybrid HMM/NN system improves by about 10% the word recognition rate over the HMM system alone.
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