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  • Cursive Character Segmentation Using Neural Network Techniques

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    Author(s)
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Year published
    2008
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    Abstract
    The segmentation of cursive and mixed scripts persists to be a difficult problem in the area of handwriting recognition. This research details advances for segmenting characters in off-line cursive script. Specifically, a heuristic algorithm and a neural network-based technique, which uses a structural feature vector representation, are proposed and combined for identifying incorrect segmentation points. Following the location of appropriate anchorage points, a character extraction technique, using segmentation paths, is employed to complete the segmentation process. Results are presented for neural-based heuristic segmentation, ...
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    The segmentation of cursive and mixed scripts persists to be a difficult problem in the area of handwriting recognition. This research details advances for segmenting characters in off-line cursive script. Specifically, a heuristic algorithm and a neural network-based technique, which uses a structural feature vector representation, are proposed and combined for identifying incorrect segmentation points. Following the location of appropriate anchorage points, a character extraction technique, using segmentation paths, is employed to complete the segmentation process. Results are presented for neural-based heuristic segmentation, segmentation point validation, character recognition, segmentation path detection and overall segmentation accuracy.
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    Book Title
    Machine Learning in Document Analysis and Recognition
    Publisher URI
    https://link.springer.com/chapter/10.1007/978-3-540-76280-5_10
    DOI
    https://doi.org/10.1007/978-3-540-76280-5_10
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/23529
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    • Book chapters

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