This work presents the application of HMM adaptation techniques to the problem of off-line cursive script recognition. Instead of training a new model for each writer one first creates a unique model with a mixed database and then adapts it for each different writer using his own small dataset. Experiments on a publicly available benchmark database show that an adapted system has an accuracy higher than 80% even when less than 30 word samples are used during adaptation, while a system trained using the data of the single writer only needs at least 200 words (the estimate is a lower bound) in order to achieve the same performance as the adapted models.


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    Title :

    Writer adaptation techniques in off-line cursive word recognition


    Contributors:


    Publication date :

    2002-01-01


    Size :

    301178 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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