Comparison of features and classifiers for detailedly lassifying handwriting characters in Chinese ink texts
面向中文矢量笔迹中单字详细分类的特征和方法比较*

Different types of characters from Chinese ink texts are recognized before they are need to different recognizers.Thus it is prerequisite to identify writing characters' detailed categories for improving their recognition.This paper aimed to classify writing characters into Chinese character,punctuation,digit,number,as well as English letter and word.Extracted each writing character' self and relative features,and applied representative classifiers,such as decision trees,logistic model trees,Bayesian network and SVM.Features and classifiers were evaluated with many real-life Chinese ink texts.Experimental results show that relative features are more powerful and SVM is the most efficient classifier for each type of writing characters.