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Handwritten digit recognition based on classical machine learning methods

Nowadays, handwritten digit recognition using deep learning and machine learning has become a popular field, and many approaches are based on CNN to achieve this. However, a recent study shows that perhaps MLP have better image classification performance compared to CNN. To investigate the performance of classical machine learning algorithms for image classification and to provide possible conclusions for subsequent research, this paper uses five classical machine learning algorithms including logistic regression, MLP, random forest, KNN and SVM to analyze, train and test experiments on handwritten digital images. The results show that MLP has better image classification performance compared to other algorithms.