Tobacco Sales Bill Recognition Based on Multi-Branch Residual Network


Yuxiang Shan, Cheng Wang, Qin Ren, Xiuhui Wang, Journal of Information Processing Systems Vol. 18, No. 3, pp. 311-318, Jun. 2022  

https://doi.org/10.3745/JIPS.02.0171
Keywords: Artificial intelligence, Image recognition, residual network
Fulltext:

Abstract

Tobacco sales enterprises often need to summarize and verify the daily sales bills, which may consume substantial manpower, and manual verification is prone to occasional errors. The use of artificial intelligence technology to realize the automatic identification and verification of such bills offers important practical significance. This study presents a novel multi-branch residual network for tobacco sales bills to improve the efficiency and accuracy of tobacco sales. First, geometric correction and edge alignment were performed on the input sales bill image. Second, the multi-branch residual network recognition model is established and trained using the preprocessed data. The comparative experimental results demonstrated that the correct recognition rate of the proposed method reached 98.84% on the China Tobacco Bill Image dataset, which is superior to that of most existing recognition methods.


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Cite this article
[APA Style]
Shan, Y., Wang, C., Ren, Q., & Wang, X. (2022). Tobacco Sales Bill Recognition Based on Multi-Branch Residual Network. Journal of Information Processing Systems, 18(3), 311-318. DOI: 10.3745/JIPS.02.0171.

[IEEE Style]
Y. Shan, C. Wang, Q. Ren, X. Wang, "Tobacco Sales Bill Recognition Based on Multi-Branch Residual Network," Journal of Information Processing Systems, vol. 18, no. 3, pp. 311-318, 2022. DOI: 10.3745/JIPS.02.0171.

[ACM Style]
Yuxiang Shan, Cheng Wang, Qin Ren, and Xiuhui Wang. 2022. Tobacco Sales Bill Recognition Based on Multi-Branch Residual Network. Journal of Information Processing Systems, 18, 3, (2022), 311-318. DOI: 10.3745/JIPS.02.0171.