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http://dx.doi.org/10.3745/JIPS.02.0171

Tobacco Sales Bill Recognition Based on Multi-Branch Residual Network  

Shan, Yuxiang (Chinese Tobacco Zhejiang Industrial Company Limited)
Wang, Cheng (Chinese Tobacco Zhejiang Industrial Company Limited)
Ren, Qin (Chinese Tobacco Zhejiang Industrial Company Limited)
Wang, Xiuhui (Dept. of Computer, China Jiliang University)
Publication Information
Journal of Information Processing Systems / v.18, no.3, 2022 , pp. 311-318 More about this Journal
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.
Keywords
Artificial Intelligence; Image Recognition; Residual Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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