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- W2171978153 abstract "This paper proposes active handwriting models, in which kernel principal component analysis is applied to capture nonlinear handwriting variations. In the recognition phase, the chamfer distance transform and a dynamic tunneling algorithm (DTA) are employed to search for the optimal shape parameters. The proposed methodology is successfully applied to a novel radical decomposition approach to the challenging problem of handwritten Chinese character recognition." @default.
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- W2171978153 date "2004-03-02" @default.
- W2171978153 modified "2023-09-22" @default.
- W2171978153 title "Nonlinear active handwriting models and their applications to handwritten Chinese radical recognition" @default.
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- W2171978153 doi "https://doi.org/10.1109/icdar.2003.1227722" @default.
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