为解决自然复杂环境中车牌的定位和识别问题，提出了一个结合车牌颜色特征、灰度特征、几何形状特征的通用车牌定位方法，并首先根据中国车牌的颜色特征，提出了一种基于距离的颜色分类方法，解决了颜色分类的基本难题；然后采用行扫描及线段分析的方法得到车牌底色所在的矩形区域；最后将可能的车牌区域统一转换成底色为黑色，字符颜色为白色的二值图像，再利用灰度特征进行验证，并分割出车牌区域。采用自然复杂环境中拍摄的图像对新方法进行验证的实验表明，与其他方法相比，新方法可在一定限度内自适应车牌的类型、大小、数量和方向，并对汽车在图像中的位置以及图像背景的限制较少，是一种适用性较强的方法。

To address the License Plates (LP) extraction in complex natural environments，a novel approach to LP extraction was presented，which utilized LP’s color information，gray feature and geometry characteristics.Firstly，a color classification method was proposed based on the distance between different colors according to the Chinese car license plate color features.Secondly，LP’s background color zones were segmented by scanning lines of picture and analyzing the line segments.Finally，these Zones were translated into binary image in which LP’s background color was dark and LP’s foreground color was white，and validated by the LP’s gray features.In a certain degree，the method could adapt to LP’s type，size，number and direction and wasn’t limited to the location of car the and the background in the picture.It was tested using the natural pictures，and was approved to be effective and adaptive.