Recognition of handwritten digits on blackboard using Resnet model

Yuxuan Zhai; Xinyi Chen

doi:10.1117/12.2635987

5 May 2022 Recognition of handwritten digits on blackboard using Resnet model

Yuxuan Zhai, Xinyi Chen

Proceedings Volume 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022); 122450T (2022) https://doi.org/10.1117/12.2635987
Event: International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 2022, Sanya, China

Abstract

Handwritten numbers play a huge role in human production and life. At present, how to automatically recognize handwritten digits accurately and efficiently has become a very realistic research problem. This article takes the handwritten digits on the blackboard as the research object, and introduces the convolutional neural network model and the Resnet model. Furthermore, a kind of recognition of handwritten digits on blackboard based on Resnet model is proposed, which can improve the recognition rate of handwritten digits by adding residual module to realize residual learning. Based on the above theory, the Resnet model is constructed using the python programming language, and the model is trained through the public data set MNIST. Finally, the model is applied to recognize the handwritten digits data set of blackboard writing, and the corresponding digits are successfully recognized.

Citation Download Citation

Yuxuan Zhai and Xinyi Chen "Recognition of handwritten digits on blackboard using Resnet model", Proc. SPIE 12245, International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), 122450T (5 May 2022); https://doi.org/10.1117/12.2635987

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