关键词: 车牌分类, 卷积神经网络, 卷积层, 降采样层

Abstract: Traditional methods of license plate location based on edge, color, texture and machine learning need complex feature extraction for license plate image. During the extraction, the training process may result in over-fitting or dimensionality?curse, in addition, recognition result is likely affected by factors like illumination, road environment, image quality, etc. Although the rate of leak recognition is low, the error rate of recognition is high. According to convolutional neural network, a deep learning algorithm can classify the license plate to avoid complex image pre-processing, which is necessary for traditional pattern classification algorithms. It also reduces the reliance on abundant experience of designing feature extraction algorithm. Convolutional neural network has better performance in license plate classification in comparison with BP neural network and support vector machine. Recognition accuracy is as high as 98.25%, which proves that deep learning algorithms have great application prospect in intelligent transportation field.

Key words: license plate classification, convolutional neural network, convolution layer, downsampling layer