Skip to main content
SpringerLink
Log in

Real-time automatic recognition of omnidirectional multiple barcodes and DSP implementation

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Barcodes have been extensively adopted in daily life, such as in merchandise labels, inventory control, storage/retrieval systems and inspection. Computer-vision-based barcode recognition can definitely facilitate barcode reading, especially for multiple barcodes and free orientation and in complex scenarios. This work, presents an automatic barcode detection and recognition algorithm for multiple and rotation invariant barcode decoding. The proposed system comprises three stages. First, the barcode is extracted by coarse-to-fine segmentation in four steps: background small clutter reduction, candidate barcode segmentation, barcode verification and barcode rotation and regularization. To enhance the barcode region, thin and small background noise clusters are eliminated using Max–Min Differencing. The approach combines several image-processing schemes, namely Gaussian smoothing filtering, connected component analysis, orientation homogeneity, moment analysis and iterative thresholding. The second stage decodes the barcode by scanning multiple traversal lines, thus preventing decoding errors due to minor barcode defects. Finally, the proposed system is implemented and optimized on a DM6437 DSP EVM board. Experimental results indicate that the proposed approach can locate multiple and omnidirectional barcodes, even with a complex background and minor distortion. The recognition rates for 10,395 lottery barcodes and 388 merchandise barcodes are 99.74 and 90.7%, respectively. The proposed system is promising and has been successfully adopted in commercial applications of lottery reading and verification of winning numbers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Sriram, T., Vishwanatha Rao, K., Biswas, S., Ahmed, B.: Applications of barcode technology in automated storage & retrieval systems. In: IECON-PROCEEDINGS, pp. 641–646 (1996)

  2. Youssef S., Salem R.: Automated barcode recognition for smart identification and inspection automation. Expert Syst. Appl. 33(4), 968–977 (2007)

    Article  Google Scholar 

  3. Ohbuchi, E., Hanaizumi, H., Hock, L.: Barcode readers using the camera device in mobile phones. In: Cyberworlds, 2004 International Conference, pp. 260–265 (2004)

  4. Lu, X., Fan, G., Wang, Y.: A robust barcode reading method based on image analysis of a hierarchical feature classification. In: International Conference on Intelligent Robots and Systems, pp. 3358–3362 (2006)

  5. Arnould, S., Awcock, G.J., Thomasm, R.: Remote bar-code localization using mathematical morphology. In: Image Processing and its Applications, 1999. Seventh International Conference (Conf. Publ. No. 465), pp. 642–646 (1999)

  6. Lee, S.J., Wang, R.C.: Barcode location determination. US Patent 4,948,955 (1990)

  7. Willsie, T.D.: Detection of barcodes in binary images with arbitrary orientation. US Patent 5,120,940 (1992)

  8. Viard-Gaudin, C., Normand, N., Barba, D.: A bar code location algorithm using a two-dimensional approach. In: Document Analysis and Recognition, 1993. Proceedings of the Second International Conference, pp. 45–48 (1993)

  9. Muniz, R., Junco, L., Otero, A.: A robust software barcode reader using the hough transform. In: International Conference on Information Intelligence and Systems, pp. 313–319 (1999)

  10. Chen, Y., Yang, Z., Bai, Z., Wu, J.: Simultaneous real-time segmentation of diversified barcode symbols in complex background. In: Intelligent Networks and Intelligent Systems, 2008. ICINIS’08. First International Workshop, pp. 527–530 (2008)

  11. Zhang, C., Wang, J., Han, S., Yi, M., Zhang, Z.: Automatic real-time barcode localization in complex scenes. In: IEEE International Conference on Image Processing, pp. 497–500 (2006)

  12. Chandler, D.G., Batterman, E.P.: Omnidirectional bar code reader with method and apparatus for detecting and scanning a bar code symbol. US Patent 5,155,343 (1992)

  13. Fang, X., Wu, F., Luo, B., Zhao, H., Wang, P.: Automatic recognition of noisy code-39 barcode. In: 16th International Conference on Artificial Reality and Telexistence, pp. 79–82 (2006)

  14. Ando, S., Hontani, H.: Automatic visual searching and reading of barcodes in 3-D scene. In: Vehicle Electronics Conference, 2001. IVEC 2001. Proceedings of the IEEE International, pp. 49–54 (2001)

  15. Kisačanin, B.: Examples of low-level computer vision on media processors. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), p. 135 (2005)

  16. Chang F., Chen C.J., Lu C.J.: A linear-time component-labeling algorithm using contour tracing technique. Comput. Vis. Image Underst. 93(2), 206–220 (2004)

    Article  Google Scholar 

  17. He L., Chao Y., Suzuki K., Wu K.: Fast connected-component labeling. Pattern Recognit. 42(9), 1977–1987 (2009)

    Article  MATH  Google Scholar 

  18. Wu K., Otoo E., Suzuki K.: Optimizing two-pass connected-component labeling algorithms. Pattern Anal. Appl. 12(2), 117–135 (2009)

    Article  MathSciNet  Google Scholar 

  19. Scotney B., Coleman S.: Improving angular error via systematically designed near-circular Gaussian-based feature extraction operators. Pattern Recognit. 40(5), 1451–1465 (2007)

    Article  MATH  Google Scholar 

  20. Mingqiang, Y., Kidiyo, K., Joseph, R.: A survey of shape feature extraction techniques. In: Yin, P.-Y. (ed.) Pattern Recognition Techniques, Technology and Applications, pp. 978–953 (2008)

  21. Gower J.C.: Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3/4), 325–338 (1966)

    Article  MATH  MathSciNet  Google Scholar 

  22. Epperson, J.: Wiley, New York (2002)

  23. Sezgin M., Sankur B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004)

    Article  Google Scholar 

  24. Texas Instrument.: Tms320dm6437 digital media processor (2006)

  25. Granston, E.: Hand-tuning loops and control code on the tms320c6000. Texas Instrument (2006)

  26. Nikolic Z., Nguyen H.T., Frantz G.: Design and implementation of numerical linear algebra algorithms on fixed point DSPs. EURASIP J. Adv. Signal Process. 2007(2), 13 (2007)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taipei University, Taipei, Taiwan

    Daw-Tung Lin

  2. Institute of Electrical Engineering, National Taipei University, Taipei, Taiwan

    Min-Chueh Lin

  3. Institute of Communication Engineering, National Taipei University, Taipei, Taiwan

    Kai-Yung Huang

Authors
  1. Daw-Tung Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min-Chueh Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kai-Yung Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daw-Tung Lin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lin, DT., Lin, MC. & Huang, KY. Real-time automatic recognition of omnidirectional multiple barcodes and DSP implementation. Machine Vision and Applications 22, 409–419 (2011). https://doi.org/10.1007/s00138-010-0299-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-010-0299-3

Keywords

Search

Navigation