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.%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.
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