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 candidate N-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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    Title :

    A hybrid large vocabulary handwritten word recognition system using neural networks with hidden Markov models


    Contributors:
    Koerich, A.L. (author) / Leydier, Y. (author) / Sabourin, R. (author) / Suen, C.Y. (author)


    Publication date :

    2002-01-01


    Size :

    330842 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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