该文介绍一种维吾尔语联机手写体识别系统。其针对维吾尔语词语的书写特点采用了基于多分类器融合的系统和方法,分别使用混合高斯模型模拟整词的静态特征和隐马尔科夫模型模拟书写笔迹的动态特征,有效地提升了识别系统的准确率。在第一期实验中,整词识别率达到97%;第二期的实验中,整词识别率达到99%。

This paper presents a system for Uyghur Online-Handwritten word recognition. According to the characteristics of the Uyghur word handwriting, the system adoptes a strategy based on multiple classifier combination, using Gaussian Mixture Model forthe static image and Hidden Markov Model for the dynamic writing trajectory of the handwritten word, respectively.The combination of multiple classifiers improves the recognition accuracy effectively. In the preliminary experiments, our system achieves an accuracy of 97% and 99%, respectively.