中文摘要：

针对字符识别对象的多样性,提出了一种基于Bagging集成的字符识别模型,解决了识别模型对部分字符识别的偏好现象。采用Bagging采样策略形成不同的数据子集,在此基础上用决策树算法训练形成多个基分类器,用多数投票机制对基分类器预测结果集成输出。理论分析与仿真实验结果表明,所提模型相比其他分类方法具有更好的分类能力。

英文摘要：

Due to the diversity of character recognition,a character recognition model based on Bagging ensemble is presented,which solves recognition model＇s preferences for certain character.Different datasets are formed by Bagging,and then base-classifier is constructed.Ensemble learning model is built by majority vote.Theoretic analysis and simulation result shows the model owns better classification accuracy than other classification methods.