摘要
根据我国车辆牌照的特点,提出了基于BP神经网络识别算法。算法中将分类器分为汉字分类器,英文字母分类器,英文字母和数字混合分类器以及数字分类器四种,这种神经网络设计可以有效简化网络结构,提高识别精度和速度。由于标准BP算法具有收敛速度慢、易陷入局部极小点等缺点,对BP算法进行了改进。通过仿真实验,该字符识别系统具有较高的识别率,同时也具备了神经网络本身容错能力强,即鲁棒性好的特点。
According to the vehicle licence in China characteristics,paper presented recognition based on BP neural network algorithm.Classifier in algorithm was divided into Chinese characters,English letter classification,English letters and numbers mixed classification,as well as for digital classification for four.The neural network structure design can effectively simplify network,improve recognition accuracy and speed.Due to the slow convergence standard BP algorithm,ease into a local minimum point disadvantage,paper improved BP algorithms.Through simulation,the character recognition system had a high recognition rate,also had a neural network fault-tolerant ability in itself,features of robustness was good.
出处
《河北省科学院学报》
CAS
2012年第1期22-27,共6页
Journal of The Hebei Academy of Sciences
关键词
字符识别系统
BP网络
分类器
Character Recognition System
BP network
Classifier
