Author(s)

Sizhe Zou ¹

Affiliation(s)

¹ Institute of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

Corresponding Author

Sizhe Zou

ABSTRACT

The technology of handwritten digit recognition has been widely applied in various situations and has significant practical significance. However, the morphological features of handwritten numbers are very complex, and achieving accurate recognition of handwritten numbers relies on efficient and accurate recognition techniques. This article proposes a residual convolutional network model to address the issues of inaccurate feature extraction and weak model generalization ability in convolutional neural networks. By introducing residual blocks into the network, the problem of vanishing and exploding network gradients is effectively eliminated. At the same time, the Batch Normalization and Dropout layers are introduced to accelerate the network training process and reduce the risk of overfitting. Finally, the k-fold cross validation method was used to select the optimal parameter configuration of the model. The experimental results show that residual convolutional neural networks have the characteristics of high recognition accuracy and strong model generalization ability.

KEYWORDS

Residual Convolution; Handwritten Digit Recognition; Neural Networks

CITE THIS PAPER

Sizhe Zou, Research on Residual Convolutional Neural Network for Handwritten Digit Recognition. Journal of Electronics and Information Science (2023) Vol. 8: 50-59. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080306.

REFERENCES

[1] Huang Rui, Lu Xuming, Wu Yilin. Cursive number recognition and application based on Tensor Flow deep learning [J]. Electron Technology Applications, 2018, 44(10):6-10. DOI:10.16157/j.issn.0258-7998.182249.
[2] He Ping, Liu Ziyan. Handwritten digit recognition based on improved multi-layer perceptron [J]. Communication Technology, 2018, 51(09):2075-2080.
[3] Tan Shuai. Research on handwritten digit recognition based on deep residual networks [D]. Xi'an University of Electronic Science and Technology, 2019.
[4] Chen Yan, Li Yangyang, Yu Le, Wang Yao, Wu Chao, Li Yangguang. Cursive number recognition system based on convolutional neural network [J]. Microelectronics and computer, 2018, 35(02):71-74. DOI:10.19304/j. cnki. issn1000-7180. 2018.02.015.
[5] Song Xiaoru, Wu Xue, Gao Song, Chen Chaobo. Simulation research on handwritten digit recognition based on deep neural networks [J]. Science Technology and Engineering, 2019, 19(05):193-196.
[6] Bu Lingzheng, Wang Hongdong, Zhu Meiqiang, Dai Wei. Multi source number recognition algorithm based on improved convolutional neural network [J]. Computer Application, 2018, 38(12):3403-3408.
[7] Huang Yitian, Chen Zhitong. Recognition of handwritten digits based on convolutional neural network in the Pytorch framework [J]. Electronic Technology and Software Engineering, 2018(19):147.
[8] Hu Junping, Fu Kexue. Research on handwritten digit recognition based on improved KNN algorithm [J]. Journal of Wuhan University of Technology (Information and Management Engineering Edition), 2019, 41(01):22-26.
[9] Yang Gang, He Dongge, Dai Lizhen. Research on handwritten digit recognition based on CNN and particle swarm optimization SVM [J]. Journal of East China Jiaotong University, 2020, 37(04):41-47. DOI:10. 16749/j. cnki. Jecjtu .2020.04.007.
[10] Lv Hong. Design of handwritten digit recognition system based on convolutional neural network [J]. Intelligent Computers and Applications, 2019, 9(02):54-56+62.
[11] He K., Zhang X., Ren S., & Sun J. Deep residual learning for image recognition [J]. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2019:770-778.
[12] Kulkarni S.R. and Rajendran B. Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization [J]. Neural networks, 2018, 103:118-127. DOI:10.1016/j.neunet. 2018.03.019.
[13] Zhang A. et al. Dive into Deep Learning [M]. 2021. doi:10.48550/arxiv.2106.11342.
[14] Kusetogullari H. et al. Digitnet: A deep handwritten digit detection and recognition method using a new historical handwritten digit dataset [J]. Big data research, 2021, 23:100-182. DOI:10.1016/j.bdr. 2020.100182.

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