基于改进BP网络的车牌字符识别方法研究

RESEARCH ON LICENSE PLATE CHARACTER RECOGNITION METHOD BASED ON IMPROVED BP NEURAL NETWORK

针对传统BP算法在车牌字符识别速度较慢和识别准确率较低的问题,提出一种改进的BP网络车牌字符识别方法。通过对BP算法的输入特征数优化,在不降低识别精度的情况下精简了输入层节点数,提升了识别速度。改进后的BP算法采用全参数自动调整,引入自适应学习率、动量因子、坡度因子,增加了BP算法的识别精度;同时通过更好的利用车牌字符特征和BP网络特征,降低了算法结构的复杂性,增强了算法的鲁棒性。实验结果表明,该算法在实际采集的自建整副车牌数据集上的识别率上比传统BP神经网络车牌识别算法提高近6.5%;在识别速度上提高近1.3 s。

Aiming at the problem that the traditional BP algorithm is slow in the recognition speed of the license plate and the recognition accuracy is low, an improved BP neural network license plate character recognition method is proposed. By optimizing the input feature number of BP algorithm, the number of nodes in the input layer is reduced and the recognition speed is improved without reducing the recognition accuracy. The improved BP algorithm adopts the all parameter automatic adjustment and introduces adaptive learning rate, momentum factor and slope steepness factor, which increases the recognition accuracy of BP algorithm. At the same time, through better use of the license plate character features and BP network features, it reduced the complexity of the algorithm structure, and enhanced the robustness of the algorithm. The experimental results show that the recognition rate of the algorithm is 6.5% higher than that of the traditional BP neural network license plate recognition algorithm based on the self-built license plate data set, and the recognition speed is improved by 1.3 s.