中文摘要：

基于唯一的形状的分析和 Uyghur 人物的写的式样，我们为原型特性识别系统设计一个框架并且在它的模块上执行系统的理论、试验性的研究。在预处理过程，我们基于点密度方法使用线性、非线性的正规化。结构、统计的特征由于在 Uyghur 文学有一些很类似的人物的事实被提取。在聚类分析，我们基于最小的跨越树(山区标准时间) 采用动态聚类算法，并且使用作为分类器匹配分类的 k 近邻居。识别为四种不同类型(独立人士，后缀，中介，和起始的类型) 的字符评估的原型系统表演的严峻的结果分别地是 74.67% ， 70.42% ， 63.33% ，和 72.02% ；为那些人物的五个候选人的盒子的识别率分别地是 94.34% ， 94.19% ， 93.15% ，和 95.86% 。在这篇论文使用的想法和方法为属于阿尔泰的语言家庭的另外的人物的识别有某公共和实用性。

英文摘要：

Based on the analysis of the unique shapes and writing styles of Uyghur characters,we design a framework for prototype character recognition system and carry out a systematic theoretical and experimental research on its modules.In the preprocessing procedure,we use the linear and nonlinear normalization based on dot density method.Both structural and statistical features are extracted due to the fact that there are some very similar characters in Uyghur literature.In clustering analysis,we adopt the dynamic clustering algorithm based on the minimum spanning tree（MST）,and use the k-nearest neighbor matching classification as classifier.The testing results of prototype system show that the recognition rates for characters of the four different types（independent,suffix,intermediate,and initial type） are 74.67%,70.42%,63.33%,and 72.02%,respectively;the recognition rates for the case of five candidates for those characters are 94.34%,94.19%,93.15%,and 95.86%,respectively.The ideas and methods used in this paper have some commonality and usefulness for the recognition of other characters that belong to Altaic languages family.