A Directed Graphical Model for Linear Barcode Scanning from Blurred Images

  • Ling Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)


Image blur is one of the major issues deteriorating the capability of a linear barcode scanning system. In this work, linear barcode scanning is treated under the perspective of stochastic modeling and inference. A directed graphical model is proposed to characterize the relationship between barcode value and its out-of-focused waveforms, based on which highly effective inference process can be implemented, allowing decoding barcode in real-time on mobile devices, directly from blurred images. The value of the proposed model is its potential to enlarge the operating range of current linear barcode scanning systems with no need for dedicated hardware components and making linear barcode scanning at close-up distance on fixed-focus lens a reality.


Information Unit Camera Phone Line Spread Function Symbol Character Reference Waveform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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. IEEE (2008)Google Scholar
  2. 2.
    Joseph, E., Pavlidis, T.: Bar code waveform recognition using peak locations. IEEE Trans. Pattern Anal. Machine Intell. 16, 630–640 (1994)CrossRefGoogle Scholar
  3. 3.
    Esedoglu, S.: Blind deconvolution of bar code signals. Inverse Problems 20, 121–135 (2004)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Dridi, N., Delignon, Y., Sawaya, W., Septier, F.: Blind detection of severely blurred 1d barcode. In: GLOBECOM, pp. 1–5. IEEE (2010)Google Scholar
  5. 5.
    Qu, L., Tu, Y.: Change point estimation of bilevel functions. Journal of Modern Applied Statistical Methods 5, 347–355 (2006)Google Scholar
  6. 6.
    Choksi, R., van Gennip, Y.: Deblurring of one dimensional bar codes via total variation energy minimization. SIAM J. Imaging Sci. 3, 735–764 (2010)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    ISO: ISO/IEC 15420:2000 Information technology — Automatic identification and data capture techniques — Bar code symbology specification — EAN/UPC. International Organization for Standardization (2000)Google Scholar
  8. 8.
    Kundur, D., Hatzinakos, D.: Blind image deconvolutions. IEEE Signal Process. Mag. 13, 43–63 (1996)CrossRefGoogle Scholar
  9. 9.
    Pavlidis, T., Swartz, J., Wang, Y.: Fundamentals of bar code information theory. Computer 23, 74–86 (1990)CrossRefGoogle Scholar
  10. 10.
    Forney, G.D.: The viterbi algorithm. Proc. IEEE 61, 268–278 (1973)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zamberletti, A., Gallo, I., Carullo, M., Binaghi, E.: Neural image restoration for decoding 1-d barcodes using common camera phones. In: Proceedings of 5th International Conference on Computer Vision Theory and Applications, VISAPP 2010, pp. 5–11 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ling Chen
    • 1
  1. 1.Department of Computer ScienceSouthwestern University of Finance and EconomicsChina

Personalised recommendations