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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    Titel :

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


    Beteiligte:
    Blumenstein, M. (Autor:in) / Verma, B. (Autor:in) / Basli, H. (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    231814 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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