Nonlinear Active Radical Modeling for Handwritten Chinese Character Recognition
手写汉字识别的非线性动态部件模板

This paper improves the authors' previously proposed nonlinear active hand-writing models and applies them into radical extraction for handwritten Chinese charac-ter recognition. In the training phase, kernel principal component analysis is used to capture nonlinear handwriting variations. Then deformable models can be generated by varying a small number of shape parameters. In the recognition phase, genetic algo-rithms, rather than dynamic tunneling algorithm in the original version, are employed to search for the optimal shape parameters. Experiments are conducted on 200 radicals covering 2154 character categories. The correct matching rate is 97. 4% on 430,800 loosely-constrained characters. Comparison with existing representative radical approa-ches shows that our method achieves superior performance.