基于组合特征和BP网络的车牌字符识别方法

Li Mingdong, Zhang Juan, Fang Zhijun
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引用次数: 1

摘要

为了提高车牌字符识别率,提出了一种基于组合特征和BP神经网络的车牌字符识别方法。首先,根据车牌字符纹理特征,对基本LBP算子进行改进;其次,将改进的局部二值模型与水平垂直投影相结合,提取车牌字符图像的特征;然后利用组合特征训练BP神经网络分类器,并将其应用于车牌字符识别。实验结果表明,车牌识别准确率达到94.55%。验证了该方法的有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
License plate character recognition method based on combination feature and BP network
In order to improve the license plate character recognition rate, a license plate character recognition method based on combination feature and BP neural network is proposed. Firstly, according to the license plate character texture features, the basic LBP operator is improved in our method. Secondly, the improved local binary model and horizontal vertical projection are combined to extract the characteristics of the license plate character image. Then the combined feature is used to train the classifier in BP neural network, and it is applied to identify license plate characters. The experimental results show that the recognition accuracy rate of the license plate reaches 94 .55% . The validity and robustness of the method are verified.
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