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Handwritten Chinese numeral recognition based on BP neural network

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DOI: 10.23977/jipta.2021.41008 | Downloads: 8 | Views: 346

Author(s)

Yigui Wang 1

Affiliation(s)

1 Jinan University College of information science and technology, Guangzhou, Guangdong, 511436

Corresponding Author

Yigui Wang

ABSTRACT

In this paper, based on pybrain library, a handwritten numeral recognition algorithm is realized by establishing BP neural network, and the accuracy and test time of the algorithm are verified by experiments. The experimental results show that after the training, the algorithm only takes 0.07 seconds in the test process of 50 groups of experimental data, and its accuracy is 98%.

KEYWORDS

BP neural networks, Chinese handwriting, digital recognition, pybrain

CITE THIS PAPER

Yigui Wang. Handwritten Chinese numeral recognition based on BP neural network. Journal of Image Processing Theory and Applications (2021) Vol. 4: 51-55. DOI: http://dx.doi.org/10.23977/jipta.2021.41008.

REFERENCES

[1] Ren Dan, Chen Xuefeng. Principles and Applications of Handwritten Digital Recognition (J.Computer Age, 2007 (03): 17-18 s. 21.
[2] Chen Hongyu. Research and implementation of handwritten digital recognition technology based on KNN algorithm.
[3] Mei Wei; Liao Wei; Yu Wanling. Research on Handwritten Digital Recognition Technology Based on the Lennet Neural Network. New Technology New Process, 2020, (06): 51-53.
[4] Ellie Wang; Xue Dong; Wu Haihai; Wang Minhui. Condition-generated anti-network handwritten digital recognition . . . liquid crystal and display, 2020, (12): 1284-1290.
[5] Liu Chenyu. Research and Design of Handwritten Digital Recognition Based on Convolutional Neural Networks. Chengdu University of Technology, 2018.
[6] Gao Guangdong. Handwritten digital recognition application based on convolutional neural network.
[7] Through the city, Yang Yunfeng. Comparison of the accuracy of handwritten digital recognition based on four algorithms. New Industrialization, 2020, 10 (07): 1-3.
[8] Tang Xiaowu. The design and implementation of a handwritten digital classifier based on KNN algorithm.
[9] Song Xiaoru, Wu Xue, Gao Hao, Chen Chaobo. Handwritten Digital Recognition Simulation Research based on Deep Neural Networks . . . Science and Technology and Engineering, 2019, 19 (05): 193-196.
[10] Yu Shengxin, Xia Chengxuan, Tang Zexuan, Ding Zhao, Yang Chen. Based on improved handwriting digital recognition of the Inception convolutional neural network. Computer Applications and Software, 2019, 36 (12): 143-149.
[11] Lan Jia, Hongxia Miao, Bensheng Qi. Studying on Improved Spiking Neural Network in Handwritten Digital Recognition [J]. IOP Conference Series: Earth and Environmental Science, 2019, 252(2):
[12] Ma Yichao, Zhao Yunji, Zhang Xinliang. CNN Handwritten Digital Recognition Algorithm based on PCA Initial Convolution Cores (J Computer Engineering and Applications, 2019, 55 (13): 134-139.

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