An applied coarse classification scheme and analysis
Hongrui Li, Fang Ou Yang, Lina Zuo, Xuedong Tian · 2008
An applied coarse classification scheme for handwritten Chinese character is presented in this paper. Four-side code feature is employed as coarse feature and RBF neural network is used as classifier in this experiment. In contrast to Euclidean distance as the measurement of similarity used in conventional method, RBF (radial basis function) neural network is better to fit the data of each class. In this way the precision rate is up to 93.20%. Analyzing the misclassified characters, overlap area classification method is applied in experiment and precision rate is up to 96.18%. Experimental results show that proposed method is applied and has satisfying performance on coarse classification of handwritten Chinese character.