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
2003-01-01
260863 byte
Conference paper
Electronic Resource
English
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
|