关键词: 手写识别, 数据挖掘, 图文分离, 分类模型

Abstract: As a technology to improve human-computer interaction， handwriting recognition is becoming more and more important. However， the distinction of handwriting texts and shapes has not drawn enough attention. In this paper， we designed and implemented a handwriting text and shape separation approach based on Weka. The experiment results based on three classification techniques， LogitBoost， RandomForest and LogitBoost， show that LogitBoost performances best. Through a combination of these three classifiers， shapes can be recognized more accurately， while the precision of text is limited by the classifier with lowest accuracy. Moreover， the effect of different features to the results is analyzed based on Information Gain Method.

Key words: sketch recognition, data mining, text-shape separation, classification model