基于Gabor变换和LMBP神经网络的车牌汉字字符识别

Recognition of License Plate Chinese Character Based on Gabor Filters and BP Neural Networks

字符识别是汽车牌照自动识别系统中的关键环节,汉字字符识别是其中的难点。提出用Gabor滤波器对灰度汉字图像抽取横、竖、撇、捺的4幅能量特征图像的方法,同时对Gabor滤波器组输出值进行非线性变换,使其适应于不同亮度和低质量灰度车牌字符图像的识别,最终采用网络法提取4幅能量特征图像的特征,用改进的BP神经网络作为车牌汉字字符的识别器,提高车牌识别率。

Character recognition is the key of recognition systems for license plate, and Chinese character recognition is the most difficult point. A kind of method that the gray character image is transformed into horizontal, vertical, chargeoffs, Na energy feature image by Gabor filters is proposed. Non-linear transformation is designed to regulate the outputs of Gabor filters adaptively which is used to improve the performances for low quality images. Finally we rely on network method for feature extraction and advanced BP neural network as the classification to enhance the rate of the recognition of license plate Chinese character.