Abstract: According to the features of Uygur letters, the author posed a method of identifying the stroke edge quantification model of handwriting. On the basis of extracting edge image, a text-independent, direction and length-dependent feature structure vector model was set up on the four-class angles tendency of Uygur letter handwriting edge in using the basic stroke concept of “horizontal, vertical, left-descending and right-descending strokes”. The author counted all feature structures of local windows to obtain the probability density feature vector of the edge stroke, used weighted and unweighted distance formulas to get the handwriting feature vector distance between identification and reference samples and judged candidate writers by sorting vector distance. With a relative strong practicability, this method can describe local feature and style of Uygur letter handwriting commendably and achieve better identifying results.

Key words: handwriting identification, Uygur, edge stroke feature structure, text independent, vector model, weighted distance

摘要： 针对维吾尔文字的特点提出一种笔迹边缘量化模型的鉴别方法。该方法在提取边缘图像的基础上，以“横竖撇捺”基本笔画概念对维吾尔文字笔迹边缘在四族角度趋向上建立一种与文本无关、与方向和长度相关的特征结构矢量模型，统计所有局部窗口的特征结构并得到边缘笔画的概率密度特征向量，使用加权与不加权的距离公式求得鉴别样本笔迹与参考样本笔迹间的特征向量距离，通过比对向量距离来筛选笔迹的候选书写者。该方法能很好地刻画维吾尔文字的笔迹的局部的特征和风格，有较强的实用性，并取得了较好的鉴别效果。

关键词: 笔迹鉴别, 维吾尔文, 边缘笔画特征结构, 文本无关, 矢量模式, 加权距离