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.
Nonlinear active handwriting models and their applications to handwritten Chinese radical recognition
01.01.2003
260863 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2003
|A Radical Approach to Handwritten Chinese Character Recognition Using Active Handwriting Models
British Library Conference Proceedings | 2001
|British Library Online Contents | 1995
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