基于改进隐马尔可夫特征的车牌识别技术
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摘要
为实现复杂光照条件及字符图像存在旋转、遮挡和污损等情况下车牌字符的准确识别,提出基于改进隐马尔可夫特征的车牌字符识别算法,算法通过快速独立成分分析对隐马尔可夫特征降维,减少参与神经网络分类的特征维数从而提高识别效率,通过选取代表性训练样本参与分类器训练,减少算法对硬件性能的要求从而进一步提高算法的识别效率。实验结果表明,该算法在保持原有统计特征分类识别性能的条件下,显著减少了运行时间,提高了识别准确率。
In order to realize the accurate recognition of the license plate characters under conditions of complex illumination and the character with rotation,occlusion or fouling,a new License plate character recognition algorithm based on Improved Hidden Markov features is proposed,in which the recognition efficiency is improved by reducing the dimension of the features using the fast independent component analysis and By choosing representative training samples to participate in classifier training through which Reduces the requirements for hardware performance.The experimental results show that,the proposed algorithm can significantly reduce the running time and improve the recognition accuracy under the condition of keeping the original classification and recognition performance.
In order to realize the accurate recognition of the license plate characters under conditions of complex illumination and the character with rotation,occlusion or fouling,a new License plate character recognition algorithm based on Improved Hidden Markov features is proposed,in which the recognition efficiency is improved by reducing the dimension of the features using the fast independent component analysis and By choosing representative training samples to participate in classifier training through which Reduces the requirements for hardware performance.The experimental results show that,the proposed algorithm can significantly reduce the running time and improve the recognition accuracy under the condition of keeping the original classification and recognition performance.
引文
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[15]RINEN A,OJA E.A fast fixed-point algorithm for Independent component analysis[J].Neural computation,1997,9(7):1483-1492.
[2]翟国庆,李汪佩.渣土车车牌字符智能识别研究[J].电子测量与仪器学报,2016,30(12):1853-1860.
[3]邹明明,卢迪.基于改进模板匹配的车牌字符识别算法实现[J].国外电子测量技术,2010,29(1):59-61.
[4]张旭兰.基于BP人工神经网络的车牌字符识别优化算法[J].计算机工程与应用,2012,48(35):182-185.
[5]郭荣艳,胡雪惠.BP神经网络在车牌字符识别中的应用研究[J].计算机仿真,2010,27(9):299-350.
[6]林川,吴正茂,覃金飞.基于机器视觉的分级车牌字符识别方法[J].电视技术,2014,38(11):198-201.
[7]牛博雅,黄琳琳,胡健.自然场景下的车牌检测与识别算法[J].信号处理,2016,32(7):787-794.
[8]费继友,谢金路,李花,等.基于字符特征约束的自适应车牌校正提取[J].仪器仪表学报,2016,37(3):632-639.
[9]胡金蓉,王玲.基于阈值分割的运矿车辆车牌精确定位[J].计算机工程与设计,2009,30(8):2051-2054.
[10]胡金蓉,周激流,王玲,等.一种新的基于字符形状特征的高效车牌识别算法[J].计算机应用研究,2010,27(6):2398-2400.
[11]王忠飞,陈元正.基于轮廓特征的车牌英文和数字识别方法[J].浙江工业大学学报,2015,43(5):522-526.
[12]耿庆田,赵宏伟.基于分形维数和隐马尔科夫特征的车牌识别[J].光学精密工程,2013,21(12):3198-3204.
[13]HYVRINEN A,HURRI J,HOYER P O.Independent component analysis[M].IEEE Press,2004.
[14]何川,舒勤,李旻.基于改进单通过FastICA的谐波与间谐波检测[J].电网技术,2013,37(10):2959-2964.
[15]RINEN A,OJA E.A fast fixed-point algorithm for Independent component analysis[J].Neural computation,1997,9(7):1483-1492.