Dynamic clustering algorithm classifying same Chinese character based on global features
利用总体特征对中文签字进行分类的聚类算法

To classify the same Chinese characters written by different writers, a dynamic clustering algorithm was presented in this paper. The algorithm was based on C-means and Mahalanobis distance. Firstly, the patterns were classified using C-means based Euclidean distance. Secondly, according to the value of a principal function, the class of each pattern was adjusted. At last, based on the initial classes, all patterns were classified again based on Mahalanobis distance. Except the clustering algorithm, the selection of character features was discussed too. Experiments result showed it is very important to select a set of features that represent accurately the handwritten style of writer. A promising result has been obtained by using this algorithm to classify same characters written by different writers.