河南科技大学车辆与交通工程学院 洛阳 471000
TN2
College of Vehicle and Traffic Engineering, He’nan Science and Technolgy University, Luoyang 471000, China
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.
罗栩豪,王培,肖怀成,李帅,孙冬冬.基于改进隐马尔可夫特征的车牌识别技术[J].国外电子测量技术,2017,36(9):99-103
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