基于卷积神经网络的车牌字符识别

车牌字符识别是智能车牌识别系统中的重要组成部分。针对车牌字符类别多、背景复杂影响正确识别率的问题，提出了一种基于卷积神经网络（CNN）的车牌字符识别方法。首先对车牌字符图像进行大小归一化、去噪、二值化、细化、字符区域居中等预处理，去除复杂背景，得到简单的字符形状结构；然后，利用所提出的CNN模型对预处理后的车牌字符集进行训练、识别。实验结果表明，所提方法能够达到99.96%的正确识别率，优于其他三种对比方法。说明所提出的CNN方法对车牌字符具有很好的识别性能，能满足实际应用需求。

Character recognition of license plate based on convolution neural network

Character recognition of license plate is an important component of an intelligent license plate recognition system. Both the number of categories and the complexity of background of license plate character affected the correct recognition rate. A character recognition method of license plate based on Convolution Neural Network (CNN) was proposed for improving the correct recognition rate. Firstly, the simple shape structures of license plate characters were obtained through image preprocessing which included image size normalization, image denoising, image binarization, image thinning, and character centering. Secondly, the preprocessed character images were trained and recognized by the proposed CNN model. The experimental results show that the correct recognition rate of the proposed method can reach 99.96%, which is better than the other three compared methods. It is demonstrated that the proposed CNN method has good recognition performance for the license plate character, and can meet the practical application requirements.