Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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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)
  • Received : 2022.02.15
  • Accepted : 2022.04.12
  • Published : 2022.06.30
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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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Acknowledgement

This work was supported by the Research on Key Technology and Application of Marketing Robot Process Automation (RPA) Based on Intelligent Image Recognition of the Zhejiang China Tobacco Industry Co. Ltd. (No. ZJZY2021E001).

References

  1. K. Ohri and M. Kumar, "Review on self-supervised image recognition using deep neural networks," Knowledge-Based Systems, vol. 224, article no. 107090, 2021. https://doi.org/10.1016/j.knosys.2021.107090
  2. W. Ma, X. Tu, B. Luo, and G. Wang, "Semantic clustering based deduction learning for image recognition and classification," Pattern Recognition, vol. 124, article no. 108440, 2022. https://doi.org/10.1016/j.patcog.2021.108440
  3. Y. Zhou, "Vehicle image recognition using deep convolution neural network and compressed dictionary learning," Journal of Information Processing Systems, vol. 17, no. 2, pp. 411-425, 2021. https://doi.org/10.3745/JIPS.01.0073
  4. Q. Chen, W. Zhang, K. Zhu, D. Zhou, H. Dai, and Q. Wu, "A novel trilinear deep residual network with self-adaptive Dropout method for short-term load forecasting," Expert Systems with Applications, vol. 182, article no. 115272, 2021. https://doi.org/10.1016/j.eswa.2021.115272
  5. J. Chen, J. Hu, and S. Li, "Learning to locate for fine-grained image recognition," Computer Vision and Image Understanding, vol. 206, article no. 103184, 2021. https://doi.org/10.1016/j.cviu.2021.103184
  6. Y. Zhao, C. Wang, J. Pei, and X. Yang, "Nonlinear loose coupled non-negative matrix factorization for low-resolution image recognition," Neurocomputing, vol. 443, pp. 183-198, 2021. https://doi.org/10.1016/j.neucom.2021.02.068
  7. Z. Zhang, P. Wang, H. Guo, Z. Wang, Y. Zhou, and Z. Huang, "DeepBackground: metamorphic testing for deep-learning-driven image recognition systems accompanied by background-relevance," Information and Software Technology, vol. 140, article no. 106701, 2021. https://doi.org/10.1016/j.infsof.2021.106701
  8. K. Huang, S. Li, W. Deng, Z. Yu, and L. Ma, "Structure inference of networked system with the synergy of deep residual network and fully connected layer network," Neural Networks, vol. 145, pp. 288-299, 2022. https://doi.org/10.1016/j.neunet.2021.10.016
  9. L. Yan, J. Feng, T. Hang, and Y. Zhu, "Flow interval prediction based on deep residual network and lower and upper boundary estimation method," Applied Soft Computing, vol. 104, article no. 107228, 2021. https://doi.org/10.1016/j.asoc.2021.107228
  10. T. Kim, K. Hong, and H. Byun, "The feature generator of hard negative samples for fine-grained image recognition," Neurocomputing, vol. 439, pp. 374-382, 2021. https://doi.org/10.1016/j.neucom.2020.10.032