摘要
为解决车牌中汉字识别未考虑汉字结构特征的问题,提出联合方向梯度直方图特征(HOG)结合支持向量机(SVM)的车牌识别算法。将灰度图、二值图、16值图的HOG特征在一定的权重下融合为联合HOG特征,使用核主成分分析法(KPCA)对联合HOG特征进行降维;对汉字和数字字母分别利用支持向量机进行分类,利用交叉验证方法对参数进行优化,得到最优预测模型;利用预测模型预测识别结果。实验结果表明,相对于传统车牌识别算法,该算法可以应用于复杂环境下的车牌字符识别,车牌识别率提高了10%左右,鲁棒性强且便于硬件实现。
A license plate character recognition method was proposed combining a joint histogram of oriented gradients （HOG） feature with support vector machine （SVM）to solve the problem that Chinese characters structure are not considered.Firstly, under a certain weight,HOG features of grayscale,binary image,16 value image were integrated into the joint HOG features, the kernel principal component analysis （KPCA）was used to reduce the dimension.Then the support vector machine was used to classify characters and alphanumeric,the cross-validation method was used to optimize the parameters,and the optimal prediction model was obtained.Finally,the predictive model was used to predict the recognition results.Experimental results show that compared with the traditional plate recognition algorithm,the new algorithm can be applied to license plate character recognition in complex environments.The license plate recognition rate is increased by about 10%,and it also has a strong robustness and is convenient for hardware implementation.
出处
《计算机工程与设计》
北大核心
2015年第2期476-481,共6页
Computer Engineering and Design
基金
高等学校博士学科点专项科研基金项目(20110141110044)
