基于神经网络的分阶车牌字符识别算法研究

为了提高复杂环境下车牌字符的识别率和识别速度,提出了一种基于BP神经网络和卷积神经网络(CNN)的分阶车牌字符识别算法。该算法第一阶段采用BP神经网络对车牌中的汉字、非相似字符进行识别;并在第二阶段用改进的CNN对车牌中的相似字符进行识别。最后通过实验横向、纵向对比,验证了该神经网络算法的有效性。实验结果表明,相对于传统的BP神经网络算法,明显提高了车牌字符的识别率,同时减少了车牌的识别时间。

Research on a phased license plate character recognition algorithm based on neural network

In order to increase the license plate recognition rate and recognition speed in complex en-vironments, a phased license plate recognition algorithm based on BP neural network and convolution neural network( CNN) is proposed. This method BP neural network used to recognize license plate charac-ters, non-similar letters and numbers in the first stage;and improved CNN used to identify similar license plate letters and numbers in the second stage. Finally through the experimental results of vertical and hor-izontal comparison, the advantage of this method is obtained. Experimental results show that compared with other algorithms such as BP neural network, this method has improved the recognition rate while the recognition time is reduced.