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  • Enhancing neural confidence-based segmentation for cursive handwriting recognition

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    29063.pdf (88.38Kb)
    Author(s)
    Cheng, Chun Ki
    Liu, Xin Yu
    Blumenstein, Michael
    Muthukkumarasamy, Vallipuram
    Griffith University Author(s)
    Blumenstein, Michael M.
    Muthukkumarasamy, Vallipuram
    Cheng, Chun Ki K.
    Liu, Xin Yu Y.
    Year published
    2004
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    Abstract
    This paper proposes some directions for enhancing a neural network-based technique for automatically segmenting cursive handwriting. The technique fuses confidence values obtained from left and center character recognition outputs in addition to a Segmentation Point Validation output. Specifically, this paper describes the use of a recently proposed feature extraction technique (Modified Direction Feature) for representing segmentation points and characters to enhance the overall segmentation process. Promising results are presented for Segmentation Point Validation and cursive character recognition on a benchmark dataset. ...
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    This paper proposes some directions for enhancing a neural network-based technique for automatically segmenting cursive handwriting. The technique fuses confidence values obtained from left and center character recognition outputs in addition to a Segmentation Point Validation output. Specifically, this paper describes the use of a recently proposed feature extraction technique (Modified Direction Feature) for representing segmentation points and characters to enhance the overall segmentation process. Promising results are presented for Segmentation Point Validation and cursive character recognition on a benchmark dataset. In addition, a new methodology for detecting segmentation paths is presented and evaluated for extracting characters from cursive handwriting.
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    Conference Title
    SEAL 04 and 2004 FIRA Robot world congress
    Publisher URI
    http://www.kaist.edu/edu.html
    http://www.cit.gu.edu.au/
    Copyright Statement
    © The Author(s) 2007 Griffith University. The attached file is posted here with permission of the copyright owner for your personal use only. No further distribution permitted.
    Publication URI
    http://hdl.handle.net/10072/2089
    Collection
    • Conference outputs

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