摘要： 手写设备用户容易忘记特定中文单字写法，需要为其提供拼音输入法。采用分类器融合方式构筑拼音单词识别系统，通过隐马尔可夫模型分类器获得拼音单词的切分点，利用统计特征识别模块进行识别后融合，研究并改进拼音单词基线提取方法。实验结果表明，该方法对17 745个测试样本的识别率达91.37%。

关键词: 中文信息处理, 字符识别, 基线

Abstract: Handwritten device users are easy to forget how to write a certain Chinese character. It is necessary to provide Pinyin input method for them. This paper constructs a Pinyin word recognition system through classifier fusion style. It obtains the cutting point of Pinyin word by Hidden Markov Model(HMM) classifier, accomplishes after-recognition fusion by using recognition module for statistic characteristic, studies and improves the base line extraction method for Pinyin word. Experimental results show that this method can recognize 91.37% test samples from 17 745 ones.

Key words: Chinese information processing, character recognition, base line

中图分类号: 

• TP391