中文摘要：

通过分析维吾尔文字母自身的结构和书写特点，提出一种联机手写维吾尔文字母识别方案，并选择在手写汉字识别技术中所提出来的归一化、特征提取及常用的分类方法，从中找出最佳的技术选择．在实验对比中，采用8种不同的归一化预处理方法，基于坐标归一化的特征提取（NCFE）方法，以及改进的二次分类函数（MQDF）、判别学习型二次判别函数（DLQDF）、学习矢量量化（LVQ）、支持向量机（SVM）4种分类器．同时，再考虑字符在文档中的空间几何特征，进一步提高识别性能．在128个维吾尔文字母类别、38400个测试样本的实验中，正确识别率最高达89．08％，为进一步研究面向维吾尔文字母特性的识别技术奠定重要基础．

英文摘要：

An approach for online handwritten Uyghur character recognition is proposed based on the analysis oi the unique shapes and writing styles of Uyghur characters. The various techniques of normalization, feature extraction and classification are evaluated that have been successfully applied in handwritten Chinese character recognition. Specifically, eight normalization techniques and the normalization cooperated feature extraction （NCFE） method with different settings are used. Four classifiers are used for classification including the modified quadratic diseriminant function （MQDF）, the discriminative learning quadratic discriminant function （DLQDF）, the learning vector quantization （LVQ） classifier, and the support vector classifier with RBF kernel （SVC-rbf）. Furthermore, the geometric features which characterize the spatial context in handwritten documents are extracted for enhancing the recognition performance. In experiments on 38 400 test samples of 128 classes, the proposed approach achieves an accuracy of 89.08%.