车牌识别中灰度模板的获得

计算机进行车牌识别，一般来说分为车牌定位，字符分割，和字符识别三个阶段。其中的字符识别，可以采用模板匹配的方法进行。如果分割后的字符较小，模板匹配可能有较大的偏差。为了解决这一个问题，人们提出了“模糊模板”的概念。实际上“模糊模板”，本质是一种灰度模板。本文将重点讨论灰度模板的获得办法。
模糊模板和灰度模板的区别，只是所关注的角度不同。 “模糊模板”从概率的角度出发，在特定的某一个坐标(x,y)上前景出现的概率定义为这一点的模板值。因为本文关注的是“小字符”的方面，对于较小的模板，可以通过缩小大模板获得。对于小模板上的点(x,y)，可能对应多个大模板上的点。这些大模板上的点，并非铁板一块的全是前景，或者全是背景。灰度模板某一点的模板值定义为：该点对应的大模板上的前景点数除以该点对应的大模板上的点数。值得注意的是大小模板尺寸都固定的时候，小模板上的一点，对应的大模板上的点数未必固定。比如宽度为64的大模板，缩小成宽度为10的小模板。小模板上的一个像素可能对应6~7个大模板像素的宽度。这个情况下上诉定义仍然成立，但是这便给缩小模板带来了一定的困难。
一般地，对于一个宽度为big_width的大模板缩小为small_width的小模板。对于小模板上的x，对应到大模板上的范围是（按c语言习惯角标从0开始）：
big_x_start=x*(big_width-1)/small_width;
big_x_end=(x+1)*(big_width-1)/small_width;
根据上面的公式，当宽度为64的大模板，缩小成宽度为10的小模板的时候，小模板上得像素情况是：

小模板上的点	0	1	2	3	4	5	6	7	8	9
对应大模板上的起点终点	0,6	6,12	12,18	18,25	25,31	31,37	37,44	44,50	50,56	56,63
对应大模板上的长度	7	7	7	8	7	7	8	7	7	8

从表中可以看出虽然最后一行的总和大于大模板的总宽度，但是表格中的第二行数据比较均匀。
对于高度，情况类似，不再累述。
下面举一个例子，

原始车牌图像

车牌字符'8'的灰度图像（为了便于观察已经放大）

大模板，来源于《GA 36-2007 中华人民共和国机动车号牌.pdf》，经过旋转处理

生成的小模板（为了便于观察已经放大）

车牌字符'8'的灰度图像和小模板的对比（为了便于观察已经放大）
图中白色代表匹配，
黄色代表模板上有笔画而实际图像没有，
青色实际图像有笔画模板上没有

最后一幅图的亮度，就是匹配度。
附录
大模板的零幺矩阵

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小模板的灰度矩阵

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读者如果正在研发车牌识别系统的话，还可以参考一下绿睿车牌识别。其识别率和识别速度还是不错的。目前这款高性能软件市场参考价只有￥210。