Research on handwriting recognition method based on machine learning

Handwritten digit recognition is a process of identifying zero to nine ten digits handwritten by human hands, and its related research has always been a hot topic in the field of machine learning classification. In order to explore the accuracy of the classification recognition of handwriting bodies by K nearest neighbor classifier and MLP multilayer perceptron, this paper first introduces the relevant algorithm principle and its research progress, and then experiments on K nearest neighbor classifier and MLP multilayer perceptron and summarizes the relevant experimental data. Experiments show that in the K nearest neighbor algorithm, the classification accuracy is the highest when the number of neighbors K=3; for the MLP multilayer perceptron algorithm, the classification rate is higher when the number of neurons is larger, the number of iterations is 1000, and the learning rate is smaller.