【摘要】 传统的车牌识别技术不仅对硬件要求高，而且识别正确率和速度均存在不足。卷积神经网络近年在图像识别领域受到关注。对AlexNet卷积神经网络结构的层次、参数等方面进行改进和重构，提出一种新的车牌识别方法。对新方法进行实验，结果表明，对于自然场景下的车牌识别，该方法可避免车牌字符分割、复杂背景环境对识别结果的影响，识别准确率高达98.03%，具有较高的可靠性。更多还原

【Abstract】 Traditional license plate recognition technology not only requires high hardware,but also has shortcomings in recognition accuracy and speed. In recent years,convolutional neural networks has attracted more and more attention in the field of image recognition. By improving and reconstructing the structure of AlexNet convolution neural network,a new license plate recognition method is proposed. The experimental results show that the method can avoid the impact of license plate character segmentation and complex background environment on the recognition results for the license plate recognition in natural scenes,and the recognition accuracy rate is as high as 98.03%,which has high reliability.更多还原