基于SVM和BP神经网络的车牌识别系统

针对车牌识别系统的车牌精确定位和车牌字符准确识别问题。提出一种基于SVM(支持向量机)和BP神经网络的车牌定位与识别算法。通过将HSV颜色空间和形态学方法相结合确定候选轮廓,以判断轮廓外接矩形的面积和长宽比筛选符合车牌特征的区域,并利用训练好的SVM模型对候选车牌区域进行测试判断,最终精确定位车牌的位置。此外,还可使用了BP神经网络进行车牌字符识别。经验证,该系统适用于复杂的车牌定位环境,且识别速度快,准确率高。

Plate Recognition System Based on SVM and BP Neural Network

This paper presents an effective license plate recognition algorithm based on SVM and BP neural network for accurate license plate location and character recognition.First,plate regions that conform to the license plate characteristics are preliminary located by the contour rectangle area with the aspect ratio acquired by HSV color model and morphological methods.Then the more precise plate region is located by SVM machine learning.Finally,the plate characters are classified by the BP neural network.Experiment results show that this system is good in accuracy and speed and suitable for complex environment.