Handwritten Chinese character recognition using Kernel active handwriting model

This paper describes a kernel active handwriting model (K-AHM) and its application to handwritten Chinese character recognition. In the model, the kernel principal component analysis is applied to capture nonlinear variations caused by handwriting, and a fitness function on the basis of chamfer distance transform is introduced to search for the optimal shape parameters using genetic algorithms (GAs). The K-AHM is applied to handwritten Chinese character recognition, which converts the complex pattern recognition problem to recognizing a small set of primitive structures call radicals. Treating Chinese character composition as a discrete-time Markov process, the character composition is carried out with the Viterbi algorithm. The proposed methodology has been successfully implemented in an experimental recognition system.