Elman递归神经网络在车牌字符识别中的应用

Application of Elman Recursive Neural Network to Recognition of Vehicle License Plate

为了提高车牌自动识别系统的速度和准确度,采用适应性较强的十三特征提取法进行车牌字符的特征提取,将提取的特征向量作为网络的输入;在对网络进行训练时,选用具有一个承接层作为一步延迟算子的动态建模性质比较好的Elman递归神经网络.此网络在权值更新时不仅考虑了当前的梯度方向,而且还考虑了前一时刻的梯度方向,从而降低了网络性能对参数调整的敏感性,有效地抑制了局部极小值的出现.最后与BP网络训练的结果进行对比,结果表明Elman递归神经网络在识别速度和准确度方面都更具优越性.

In order to improve speed and accuracy of automatic vehicle identification system,a 13-characteristic extraction process is used,and the feature vectors are inputted into network.An Elman recursion neural network is selected which including an undertaking layer as a step delay operator.This network when updating the weight value not only considers the current gradient direction,but also considers the former gradient direction,thereby reducing sensitivity of the network performance parameters and effectively suppressing the emergence of local minima.Finally by comparing with BP network training,the results show in the recognition of speed and accuracy the Elman recurrent neural network has more advantages.