摘要
针对仪表液晶显示字符识别问题,提出一种结合了卷积神经网络(CNN)和支持向量机(SVM)的字符识别方法。分别采用具有并联结构的CNN模型和基于梯度方向直方图(HOG)特征的SVM方法构建基本分类器,当2个分类器的结果存在冲突时,利用CNN的softmax输出最大值判决最终结果,当其大于设定阈值时采用CNN分类器的结果,反之采用SVM分类器的结果。建立字符图像的误差模型并利用仿真方法构建了数据集用于分类器的训练和测试,给出一种基于投票原理的最优阈值的估计算法。在MNIST和仿真数据集上的测试实验结果表明,最优阈值估计算法的结果可靠,组合分类器的准确率较2种单一分类器均有提高,在实际测试系统上其准确率达到99.81%,验证了该组合分类器方法对液晶字符识别问题的有效性;在CIFAR-10数据集上的实验结果验证了该方法也可用于其他分类问题。
A combined classifier based on convolution neural network(CNN)and support vector machine(SVM)was proposed for the recognition of liquid crystal displayer(LCD)characters.Two basic classifiers were utilized to build a combined classifier for recognition.One was CNN with a parallel structure,and the other was SVM using histogram of oriented gradients(HOG)features of the character image.If a sample’s responses from two basic classifiers conflicted with each other,the maximum component of the softmax vector outputted from CNN classifier was employed to determine the final result.If it was greater than a threshold,the CNN result was adopted,otherwise the SVM result.An error model for LCD character image was presented and adopted to construct a simulation dataset for the algorithm training and test.An optimal threshold estimation algorithm based on voting principle was proposed.The combined classifier was tested on both MNIST dataset and an LCD character simulation dataset.The experimental results show that the threshold estimation result was reliable,and that the combined classifier outperformed both CNN and SVM basic classifiers.Using the method on a real test system,the accuracy rate was 99.81%.The results prove the method’s effectiveness for LCD character recognition.The experimental results on CIFAR-10 dataset show that the method can also be applied to other kinds of classifications.
作者
刘昶
徐超远
张鑫
薛磊
LIU Chang;XU Chao-yuan;ZHANG Xin;XUE Lei(School of Information Science and Engineering,Shenyang Ligong University,Shenyang Liaoning 110159,China)
出处
《图学学报》
CSCD
北大核心
2021年第1期15-22,共8页
Journal of Graphics
基金
辽宁省自然科学基金项目(20170540792)。
关键词
计算机视觉
机器学习
液晶字符识别
支持向量机
卷积神经网络
computer vision
machine learning
liquid crystal displayer character recognition
support vector machine
convolution neural network
