Nonlinear active handwriting models and their applications to handwritten Chinese radical recognition
Geok See Ng, Daming Shi, S. Richard Gunn, R.I. Damper · 2004
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.