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A neural-network-based framework for cigarette laser code identification

  • Zeheng Yang
  • Xiurui XieEmail author
  • Qiugang Zhan
  • Guisong LiuEmail author
  • Qing Cai
  • Xu Zheng
Original Article
  • 55 Downloads

Abstract

The identification of cigarette laser codes is important in distinguishing the authenticity of tobacco. However, the existing character recognition methods have limited use in the identification due to the complex background in cigarette images. To address this issue, we propose a novel neural-network-based framework in this paper. Specifically, the framework includes three major steps. Firstly, a principal component analysis neural network is designed for the inclination correction progress to overcome the strong noise interferences. Then a novel algorithm is proposed to adaptively utilize the prior partition information for better character segmentation. Finally, a CNN model is designed to extract irregular features for character identification. By doing this, the proposed framework alleviates the influence of diverse backgrounds and keeps useful features at the same time. Additionally, we give an insight analysis on the character recognition based on the proposed method. The performance of the framework is evaluated on an image set composed of 100 cigarette laser code photos, whose results demonstrate that our framework can bring about 30% improvement in recognition accuracy compared to baseline methods. The good performance indicates a huge potential of our framework on practical applications.

Keywords

Laser code identification CNN Character segmentation Inclination correction 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61806040, 61771098), the China Postdoctoral Science Foundation (Grant No. 2018M633348), and the fund from the Department of Science and Technology of Sichuan Province (Grant Nos. 2017GFW0128, 18ZDYF2268, 2018JY0578 and 2017JY0007).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest to this work.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science, Zhongshan InstituteUniversity of Electronic Science and Technology of ChinaZhongshanChina
  2. 2.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.Institute for Infocomm ResearchAgency for Science, Technology and ResearchSingaporeSingapore
  4. 4.School of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore

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