Character classification algorithm based on the low-dimensional feature-optimized model

This work focuses on training difficulty and high computational cost caused by high-resolution input image when car plate character recognition based on support vector machine is applied. The use of singular value decomposition technology is proposed with improved optimal threshold analysis strategy to select retained features. The original data set is mapped in the low-dimensional feature space and the model is trained. The same test set is used to evaluate the models trained under different feature dimensions, and the optimal feature dimension is determined to 32 dimensional features. Results showed that the algorithm can accurately classify and detect license plate characters. The proposed model achieves 99.69% accuracy of the original data set, demonstrates excellent advantages, and remarkably simplifies the model design when only 96 dimensional features are used compared with traditional linear and convolutional neural network classifiers. New methods reduce the algorithm run time by 200%, and the number of parameter storage required by the algorithm to 25%. Hence, the proposed method can significantly improve the license plate recognition system usage in resource-constrained embedded devices.