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  • Techniques for Static Handwriting Trajectory Recovery: A Survey

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    Author(s)
    Nguyen, Vu
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Nguyen, Vu
    Year published
    2010
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    Abstract
    On-line handwriting recognition systems are usually better than their off-line counterparts thanks to the accessibility of dynamic information such as stroke order, velocity, acceleration, and pressure. Whilst the exact value of velocity as well as acceleration or pressure is unlikely to be recoverable, the temporal order of the strokes or the pen trajectory is shown to be more promising for recovery. The published experimental results suggest that the recovered pen trajectory information actually improves the off-line recognition accuracy. This paper presents an overview and discussion of pen trajectory recovery methods ...
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    On-line handwriting recognition systems are usually better than their off-line counterparts thanks to the accessibility of dynamic information such as stroke order, velocity, acceleration, and pressure. Whilst the exact value of velocity as well as acceleration or pressure is unlikely to be recoverable, the temporal order of the strokes or the pen trajectory is shown to be more promising for recovery. The published experimental results suggest that the recovered pen trajectory information actually improves the off-line recognition accuracy. This paper presents an overview and discussion of pen trajectory recovery methods developed to date.
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    Conference Title
    Proceedings of the 9th IAPR International Workshop on Document Analysis Systems DAS '10
    DOI
    https://doi.org/10.1145/1815330.1815390
    Copyright Statement
    © ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceeding DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems , ISBN(978-1-60558-773-8 ) http://dx.doi.org/10.1145/1815330.1815390
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/38243
    Collection
    • Conference outputs

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