We present a system for recognizing off-line, cursive, English text, guided in part by global characteristics (style) of the handwriting. We introduce a new method for segmenting words into letters, based on minimizing a cost function. Segmented letters are normalized with a novel algorithm that scales different parts of a letter separately removing much of the variation in the writing. We use a neural network for letter recognition and use the output of the network as posterior probabilities of letters in the word recognition process. We found that using a hidden Markov Model for word recognition is less successful than assuming an independent process for our small set of test words. In our experiments with several hundred words, written by 7 writers, 96% of the test words were correctly segmented, 52% were correctly recognized, and 70% were in the top three choices.<>


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    Title :

    Recognizing off-line cursive handwriting


    Contributors:
    Yanikoglu (author) / Sandon (author)


    Publication date :

    1994-01-01


    Size :

    610477 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Recognizing Off-Line Cursive Handwriting

    Yanikoglu, B. A. / Sandon, P. A. / Institute of Electrical and Electronics Engineers; Computer Society | British Library Conference Proceedings | 1994


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