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  • Conventional vs Neuro-Conventional Segmentation Techniques for Handwriting Recognition: A Comparison

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    Author(s)
    Blumenstein, Michael
    Verma, Brijesh
    Griffith University Author(s)
    Blumenstein, Michael M.
    Verma, Brijesh K.
    Year published
    1998
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    Abstract
    The success of Artificial Neural Networks (ANNs) has been prominent in many real-world applications including handwriting recognition. This paper compares two techniques for the task of segmenting touching and cursive handwriting. The first technique uses a conventional heuristic algorithm to detect prospective segmentation points in handwritten words. For each segmentation point a character matrix is extracted and fed into a trained ANN to verify whether an appropriate character has been located. The second technique also uses a conventional algorithm for the initial segmentation process, however two ANNs are used ...
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    The success of Artificial Neural Networks (ANNs) has been prominent in many real-world applications including handwriting recognition. This paper compares two techniques for the task of segmenting touching and cursive handwriting. The first technique uses a conventional heuristic algorithm to detect prospective segmentation points in handwritten words. For each segmentation point a character matrix is extracted and fed into a trained ANN to verify whether an appropriate character has been located. The second technique also uses a conventional algorithm for the initial segmentation process, however two ANNs are used for the entire segmentation and recognition procedures. The first ANN verifies whether accurate segmentation points have been found by the algorithm and the second classifies the segmented characters. The C programming language, the SP2 supercomputer and a SUN workstation were used for the experiments. The techniques have been tested on real-world handwriting scanned from various staff at Griffith University, Gold Coast. Some preliminary experimental results are presented in this paper.
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    Conference Title
    Second IEEE International Conference on Intelligent Processing Systems
    Publisher URI
    http://www.ieee.org/portal/site
    Copyright Statement
    © 1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/19862
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    • Conference outputs

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