In this paper we report the use of discriminative training and other techniques to improve performance in a HMM-based isolated handwritten character recognition system. The discriminative training is maximum mutual information (MMI) training; we also improve results by using composite images which are the concatenation of the raw images, rotated and polar transformed versions of them; and we describe a technique called block-based principal component analysis (PCA). For effective discriminative training we need to increase the size of our training database, which we do by eroding and dilating the images to give a three-fold increase in training data. Although these techniques are tested using isolated Thai characters, both MMI and block-based PCA are applicable to the more difficult task of cursive handwriting recognition.


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    Title :

    Discriminative training for HMM-based offline handwritten character recognition


    Contributors:


    Publication date :

    2003-01-01


    Size :

    357826 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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