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

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. 1.

    Yu Q, Yan R, Tang H et al (2016) A spiking neural network system for robust sequence recognition. IEEE Trans Neural Netw Learn Syst 27(3):621–635

    MathSciNet  Article  Google Scholar 

  2. 2.

    Xie X, Qu H, Yi Z et al (2017) Efficient training of supervised spiking neural network via accurate synaptic-efficiency adjustment method. IEEE Trans Neural Netw Learn Syst 28(6):1411–1424

    Article  Google Scholar 

  3. 3.

    Liu G, Qiu Z, Qu H (2015) Computing k shortest paths using modified pulse-coupled neural network. Neurocomputing 149:1162–1176

    Article  Google Scholar 

  4. 4.

    Xie X, Liu G, Cai Q, Wei P, Qu H (2019) Multi-source sequential knowledge regression by using transfer RNN units. Neural Netw 119:151–161

    Article  Google Scholar 

  5. 5.

    Liu CL, Nakashima K, Sako H et al (2004) Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognit 37(2):265–279

    MATH  Article  Google Scholar 

  6. 6.

    Liu CL (2007) Normalization-cooperated gradient feature extraction for handwritten character recognition. IEEE Trans Pattern Anal Mach Intell 29(8):1465

    Article  Google Scholar 

  7. 7.

    Cheng AD, Yan H (1997) Recognition of broken and noisy handwritten characters using statistical methods based on a broken-character-mending algorithm. Opt Eng 36(5):1465–1479

    Article  Google Scholar 

  8. 8.

    Vithlani P (2015) Structural and statistical feature extraction methods for character and digit recognition. J Nucl Mater 127(s 2–3):146–152

    Google Scholar 

  9. 9.

    Chen L, Qi T, Du Y et al (2014) A fast and efficient algorithm for recognizing cigarette laser security code. Sci J Inf Eng 4(3):78–82

    Google Scholar 

  10. 10.

    Lecun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  11. 11.

    Kim Y (2014) Convolutional neural networks for sentence classification. Eprint Arxiv

  12. 12.

    Haijun Z, Yuzhu J, Wang H et al (2018) Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl 31:7361–7380

    Google Scholar 

  13. 13.

    Zhang H, Wang S, Xu X et al (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):1–15

    MathSciNet  Article  Google Scholar 

  14. 14.

    Zheng K, Feng W, Chen H (2010) An adaptive non-local means algorithm for image denoising via pixel region growing and merging. In: International congress on image and signal processing. IEEE, pp 621–625

  15. 15.

    Ohtsu N (2007) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  16. 16.

    Shang L, Lv JC, Yi Z (2006) Rigid medical image registration using PCA neural network. Neurocomputing 69(13–15):1717–1722

    Article  Google Scholar 

  17. 17.

    Liu G, Yi Z, Yang S (2007) A hierarchical intrusion detection model based on the PCA neural networks. Neurocomputing 70(7–9):1561–1568

    Article  Google Scholar 

  18. 18.

    López-Rubio E, Muñoz-Pérez J, Gómez-Ruiz JA (2003) Principal components analysis competitive learning. In: International work-conference on artificial neural networks. Springer, Berlin, pp 318–325

  19. 19.

    Vegas JM, Zufiria PJ (2004) Generalized neural networks for spectral analysis: dynamics and Liapunov functions. Neural Netw 17(2):233

    MATH  Article  Google Scholar 

  20. 20.

    Oja E, Oja E (1982) A simplified neuron model as a principal component analyzer. J Math Biol 15(3):267–273

    MathSciNet  MATH  Article  Google Scholar 

  21. 21.

    Yi Z, Ye M, Lv JC et al (2005) Convergence analysis of a deterministic discrete time system of Oja’s PCA learning algorithm. IEEE Trans Neural Netw 16(6):1318

    Article  Google Scholar 

  22. 22.

    Ge SS, Hang CC, Zhang T (1999) Adaptive neural network control of nonlinear systems by state and output feedback. IEEE Trans Syst Man Cybern Part B Cybern 29(6):818

    Article  Google Scholar 

  23. 23.

    Stühmer J, Cremers D (2015) A fast projection method for connectivity constraints in image segmentation. In: Energy minimization methods in computer vision and pattern recognition. Springer, pp 183–196

  24. 24.

    Fan EG (2000) Extended tanh-function method and its applications to nonlinear equations. Phys Lett A 277(4):212–218

    MathSciNet  MATH  Article  Google Scholar 

  25. 25.

    Maitra M, Chatterjee A (2008) A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst Appl 34(2):1341–1350

    Article  Google Scholar 

  26. 26.

    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27

    Article  Google Scholar 

  27. 27.

    Lee SW, Kim YJ (1996) Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network. IEEE Trans Pattern Anal Mach Intell 18(6):648–652

    Article  Google Scholar 

Download references

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).

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Xiurui Xie or Guisong Liu.

Ethics declarations

Conflict of interest

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

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yang, Z., Xie, X., Zhan, Q. et al. A neural-network-based framework for cigarette laser code identification. Neural Comput & Applic 32, 11597–11606 (2020). https://doi.org/10.1007/s00521-019-04647-2

Download citation

Keywords

  • Laser code identification
  • CNN
  • Character segmentation
  • Inclination correction