High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.


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    Title :

    A novel feature extraction technique for the recognition of segmented handwritten characters


    Contributors:
    Blumenstein, M. (author) / Verma, B. (author) / Basli, H. (author)


    Publication date :

    2003-01-01


    Size :

    231814 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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