Handwritten image recognition based on CNN algorithm

The optimization of image recognition technology provides a platform and possibility for the dissemination and protection of most of the information documents, especially font and number recognition, which is an important part of it. At present, the recognition accuracy of standard fonts and numerals has reached its maximum with continuous optimization, but the recognition of handwritten numerals still needs to be further improved. The aim of this paper is to test the correct recognition rate of handwritten numerals using a convolutional neural network model. Through the elaboration of the basic principles and structure of the neural convolutional network algorithm and the purpose of the article, the relevant test model was established. And the MINST dataset is used to train the model and test the recognition effect of handwritten numbers. Through experiments, the model used in this paper can correctly recognize most handwritten digits, thus satisfying the purpose of the experimental design, and providing new support for experiments on handwritten fonts.