Advertisement

Efficient 1D and 2D Barcode Detection Using Mathematical Morphology

  • Melinda Katona
  • László G. Nyúl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7883)

Abstract

Barcode technology is essential in automatic identification, and is used in a wide range of real-time applications. Different code types and applications impose special problems, so there is a continuous need for solutions with improved performance. Several methods exist for code localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need reliable automatic barcode localization, e.g. conveyor belts and automated production, where missed detections cause loss of profit. Our goal is to detect automatically, rapidly and accurately the barcode location with the help of extracted image features. We propose a new algorithm variant, that outperforms in both accuracy and efficiency other detectors found in the literature using similar ideas, and also improves on the detection performance in detecting 2D codes compared to our previous algorithm.

Keywords

barcode detection morphological operations bottom-hat filter distance map 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Palmer, R.C.: The Bar Code Book: Reading, Printing, and Specification of Bar Code Symbols. Helmers Pub. (1995)Google Scholar
  2. 2.
    Adelmann, R.: Toolkit for bar code recognition and resolving on camera phones - jump starting the internet of things. In: Informatik 2006 Workshop on Mobile and Embedded Interactive Systems (2006)Google Scholar
  3. 3.
    Tuinstra, T.R.: Reading Barcodes from Digital Imagery. PhD thesis, Cedarville University (2006)Google Scholar
  4. 4.
    Tekin, E., Coughlan, J.M.: An algorithm enabling blind users to find and read barcodes. In: Workshop on Applications of Computer Vision (WACV), pp. 1–8 (2009)Google Scholar
  5. 5.
    James Juett, X.Q.: Barcode localization using bottom-hat filter. NSF Research Experience for Undergraduates (2005)Google Scholar
  6. 6.
    Bodnár, P., Nyúl, L.G.: Efficient barcode detection with texture analysis. In: Proceedings of the Ninth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, pp. 51–57 (2012)Google Scholar
  7. 7.
    Bodnár, P., Nyúl, L.G.: Improving barcode detection with combination of simple detectors. In: The 8th International Conference on Signal Image Technology (SITIS 2012), pp. 300–306 (2012)Google Scholar
  8. 8.
    Chai, D.: Locating and decoding ean-13 barcodes from images captured by digital cameras. In: Fifth International Conference on Information, Communications and Signal Processing, pp. 1595–1599 (2005)Google Scholar
  9. 9.
    Lin, D.T., Lin, M.C., Huang, K.Y.: Real-time automatic recognition of omnidirectional multiple barcodes and dsp implementation. Machine Vision and Applications 22, 409–419 (2011)CrossRefGoogle Scholar
  10. 10.
    Katona, M., Nyúl, L.G.: A novel method for accurate and efficient barcode detection with morphological operations. In: The 8th International Conference on Signal Image Technology (SITIS 2012), pp. 307–314 (2012)Google Scholar
  11. 11.
    Wachenfeld, S., Terlunen, S., Jiang, X.: Robust recognition of 1-d barcodes using camera phones. In: 19th International Conference on Pattern Recognition (ICPR 2008), pp. 1–4 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Melinda Katona
    • 1
  • László G. Nyúl
    • 1
  1. 1.Department of Image Processing and Computer GraphicsUniversity of SzegedSzegedHungary

Personalised recommendations