Tobacco Retail License Recognition Based on Dual Attention Mechanism


Yuxiang Shan, Qin Ren, Cheng Wang, and Xiuhui Wang, Journal of Information Processing Systems Vol. 18, No. 4, pp. 480-488, Aug. 2022  

10.3745/JIPS.02.0177
Keywords: Attention Mechanism, Image recognition, Robot Process Automation (RPA)
Fulltext:

Abstract

Images of tobacco retail licenses have complex unstructured characteristics, which is an urgent technical problem in the robot process automation of tobacco marketing. In this paper, a novel recognition approach using a double attention mechanism is presented to realize the automatic recognition and information extraction from such images. First, we utilized a DenseNet network to extract the license information from the input tobacco retail license data. Second, bi-directional long short-term memory was used for coding and decoding using a continuous decoder integrating dual attention to realize the recognition and information extraction of tobacco retail license images without segmentation. Finally, several performance experiments were conducted using a largescale dataset of tobacco retail licenses. The experimental results show that the proposed approach achieves a correction accuracy of 98.36% on the ZY-LQ dataset, outperforming most existing methods.


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Cite this article
[APA Style]
Yuxiang Shan, Qin Ren, Cheng Wang, & and Xiuhui Wang (2022). Tobacco Retail License Recognition Based on Dual Attention Mechanism. Journal of Information Processing Systems, 18(4), 480-488. DOI: 10.3745/JIPS.02.0177.

[IEEE Style]
Y. Shan, Q. Ren, C. Wang and a. X. Wang, "Tobacco Retail License Recognition Based on Dual Attention Mechanism," Journal of Information Processing Systems, vol. 18, no. 4, pp. 480-488, 2022. DOI: 10.3745/JIPS.02.0177.

[ACM Style]
Yuxiang Shan, Qin Ren, Cheng Wang, and and Xiuhui Wang. 2022. Tobacco Retail License Recognition Based on Dual Attention Mechanism. Journal of Information Processing Systems, 18, 4, (2022), 480-488. DOI: 10.3745/JIPS.02.0177.