Handwritten Chinese character recognition based on mirror image learning and the compound Mahalanobis function
Ding Xiaoqing · Journal of Tsinghua University(Science and Technology) · 2006
In handwritten Chinese character recognition,many misclassification errors come from the confusion of similar characters.A recognition algorithm based on mirror image learning(MIL) and the compound Mahalanobis function(CMF) was developed to improve the discrimination ability of the quadratic classifier on the similar Chinese characters.The algorithm generates virtual image mirror samples from ambiguous training samples,and adjusts the classification surfaces between confused classes by an iterative learning scheme.The candidates given by the quadratic classifier are further discriminated using the compound Mahalanobis function,which efficiently reduces the classification errors.Tests on the HCL2000 database demonstrate that the algorithm effectively improves the recognition accuracy of handwritten Chinese character,reducing the misclassification rate for the test set by 20%.