摘要
为实现复杂光照及存在遮挡和污损等情况下车牌识别,提出基于隐马尔可夫特征降维和改进概率神经网络的车牌字符快速精确识别算法,算法通过非负矩阵分解对描述字符特征的高维隐马尔可夫特征进行降维,以消除高维特征矩阵信息冗余并提高特征描述准确性,通过择取代表性样本参与PNN训练,以提高算法的分类精确性,减少硬件性能需求。对比实验结果表明,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.
作者
程茜
CHENG Xi(Xi’an Eurasia College,School of Finance,Shanxi Xi’an 710065,China)
出处
《机械设计与制造》
北大核心
2018年第10期146-148,152,共4页
Machinery Design & Manufacture
关键词
车牌识别
改进隐马尔可夫特征
快速独立成分分析
概率神经网络
License Plate Characters Recognition
Improved Hidden Markov Feature
FastICA
Probabilistic Neural Network
