In this paper we describe an off-line handwritten word recognition system applied to the identification of literal french check amounts. It consists of three successive levels denoted as character, word and phrase level, each of them being related to the previous ones via conditional probability distributions. Training is done on character samples extracted from amount images which are modeled as trajectories in some feature space. At word level, guided by a dictionary, an internal character segmentation algorithm is used in order to maximize a global word probability measure. A stochastic grammar for a priori grammar generation probability of a phrase is proposed at the last level. Results obtained on a 1779 amounts data base provided by the SRTP are encouraging, showing our system open to further improvements.


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    Title :

    Stochastic trajectory modeling for recognition of unconstrained handwritten words


    Contributors:
    Saon, G. (author) / Belaid, A. (author) / Gong, Y. (author)


    Publication date :

    1995-01-01


    Size :

    379562 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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