说明：  在车牌定位方面,首先分析了我国机动车号牌的特征,然后采用基于数学形态学的车牌定位方式,接着采用了改进的Bernsen算法进行车牌图像二值化,并对采样数据进行了实验,结果表明,采用该方法车牌定位准确率高。在字符切分方面,首先根据车牌的规格确定车牌字符区域的上下边界,接着用基于第二个字符的字符切分方法对车牌进行字符切分,最后对第一个字符和最后一个字符的宽度进行调整。并对采样数据进行了实验,结果表明该方法切分速度快,同时保证了字符切分的正确率,效果良好。在字符识别方面，利用Pytorch深度学习框架，采用卷积神经网络（CNN）搭建字符识别模型，对分割出的车牌区域进行字符识别。
(In terms of license plate localization, the characteristics of motor vehicle license plates in China were first analyzed, and then a license plate localization method based on mathematical morphology was adopted. Then, an improved Bernsen algorithm was used for license plate image binarization, and experimental results were conducted on the sampled data. The results showed that the accuracy of license plate localization using this method was high. In terms of character segmentation, first determine the upper and lower boundaries of the license plate character area based on its specifications, then use a character segmentation method based on the second character to segment the license plate, and finally adjust the width of the first and last characters. And experiments were conducted on the sampled data, and the results showed that the method has a fast segmentation speed while ensuring the accuracy of character segmentation, with good results.)