Abstract: 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. 

Key words: computer vision, machine learning, liquid crystal displayer character recognition, support vector machine, convolution neural network