利用模糊统计法识别货运列车车号的研究

室外全天候货运列车车号识别系统，由于受车厢新旧程度、环境光照变化等因素影响较大，而且采集的车号图象二值化后，车号数字常常变形或缺损，因而用传统的模板匹配等方法已不能准确提取数字特征。针对该问题提出了一种利用模糊统计法计算待识别数字各方向行程的方法来提取数字的内、外轮廊特征，并引入模糊决策方法来克服特征提取过程的不确定性。现场实验结果表明，该方法对数字的变形或缺损有较强的适应能力，已取得较高识别率。该方法对其它的标准粗印刷体数字的识别也有意义。

To Recognize Bold Printed Numerical Characters with Shape Variation by Using Fuzzy Statistic Method

Affected by the wearing of frequent usage and variation of environmental brightness, binaried numerical character images of freight train become deformed and incompleted, which are sampled by freight train mark recognizing system under various weather conditions. As traditional methods, for example pattern plate matching, are not effective to extract the character features exactly, a new method called fuzzy statistic is presented according to which the numerical character's figure features inside and outside them are extracted by computing their direction routes. By the mean time the fuzzy decision method is drawn on to overcome the indefiniteness during feature extracting. After utilize in some power plant, it appeared that the method is available for character's deformed and incompleted, also high recognized rate was obtained. The method might be referred to recognize bold printed numerical characters and other courses.