• Dung D. Vo 
  • Duy T. Nguyen 
  • Hai Thanh Nguyen 
  • Viet B. Ngo 

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Barcode attached on product is to transfer information to users. In practice, many barcodes are degraded over time and they are difficult for users to recognize product information. Therefore, barcode image restoration plays an important role due to clearly showing product information for users. This paper proposed a restoration approach of barcode- EAN 13 images with different degraded characteristics such as vertical lines, blurring, dashed lines. In particular, the degraded barcode images are pre-processed for restoring before recognition, in which a radon method is applied for rotating barcode image and an Otsu segmentation method is employed to split the barcode image from an original image. Therefore, bars in each barcode image are determined for recognition of the correct barcode. Barcode image datasets are collected from different practical products with different quality for restoration before recognizing them. Experimental results show to illustrate the proposed approach for the barcode recognition is the effectiveness

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References

  1. Swartz, J. and Sharma, S. and Barkan, E. and Barkan, E. “Barcode scanning,” Scholarpedia., vol. 7, pp. 12215, 2012.
     Google Scholar
  2. Roger C. Palmer. “The Bar Code Book,” Helmers Publishing, Inc, 2007.
     Google Scholar
  3. O. Gallo, R. Manduchi “Reading 1D Barcodes with Mobile Phones Using Deformable Templates,” IEEE Trans on Pattern Anal Mach Intell., vol. 33, pp. 1834-43, 2011.
     Google Scholar
  4. S. S. Upasani, A. N. Khandate, A. U. Nikhare, R. A. Mange, and R. Tornekar, "Robust Algorithm for Developing Barcode Recognition System using Web-cam," 2016.
     Google Scholar
  5. A. Zamberletti, I. Gallo, and S. Albertini, "Robust angle invariant 1D barcode detection," the IEEE 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 160-164, 2013.
     Google Scholar
  6. Abderrahmane Namane and Madjid Arezki, "Fast Real Time 1D Barcode Detection from Webcam Images using the Bars Detection Method," Proceedings of the World Congress on Engineering (WCE), vol. 1, London, 2017.
     Google Scholar
  7. N. Hashim, N. Ibrahim, N. Saad, F. Sakaguchi, and Z. Zakaria, "Barcode recognition system," International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 2, no. 4, pp. 278-283, 2013.
     Google Scholar
  8. O. Gallo, R. Manduchi “Reading Challenging Barcodes with Cameras,” Procedings of the IEEE Workshop Appl Comput Vis, 2009.
     Google Scholar
  9. Thanh An, Vu and Thanh Hai, Nguyen, " Enhancement of CT image using image fusion," International Conference on Advanced Technologies for Communications (ATC), 2013.
     Google Scholar
  10. Tran Thi Quynh Nhu, Nguyen Thanh Hai, Ngo Thanh Dong, Nguyen Tan Nhu, “Determining the Size of a Solid Tumor,” The 5th International Conference on the Development of Biomedical Engineering in Vietnam, 2014.
     Google Scholar
  11. P. N. Tra, N. T. Hai, and T. T. Mai, "Image segmentation for detection of benign and malignant tumors," in proceedings of International Conference on Biomedical Engineering (BME-HUST), pp. 51-54, 2016.
     Google Scholar
  12. Hamid R. Tizhoosh, Christopher Mitcheltree, Shujin Zhu, Shamak Dutta “Barcodes for medical image retrieval using autoencoded Radon transform,” the 23rd International Conference on Pattern Recognition (ICPR), 2016.
     Google Scholar
  13. N. Senthilkumaran, Thimmiaraja, "Histogram Equalization for Image Enhancement Using MRI brain images", WCCCT, pp. 80 - 83, 2014
     Google Scholar
  14. M. S. Sindhuri and N. Anusha, "Text Separation in Document Images through Otsu’s Method", International Conference on Wireless Communications, pp. 2395 - 2399, 2016.
     Google Scholar
  15. Mengxing Huang, Wenjiao Yu and Donghai Zhu, "An Improved Image Segmentation Algorithm Based on the Otsu Method ", International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel, pp. 135 - 139, 2012
     Google Scholar
  16. N. Otsu, "A threshold selection method from grey-level histograms," IEEE transactions on systems, man, and cybernetics, vol. 9, pp. 62-66, 1979.
     Google Scholar
  17. J. Li, W. Yi-Wen, Y. Chen, G. Wang. “Adaptive Segmentation Method for 2-D Barcode Image Base on Mathematic Morphological,” Research Journal of Applied Sciences, Engineering and Technology, vol. 6, pp. 3335-3342, 2013.
     Google Scholar
  18. Ruwan Janapriya, Lasantha Kularatne, Kosala Pannipitiya, Anuruddha Gamakumara, Chathura de Silva and Nalin Wickramarachchi. “An Intelligent Algorithm for Utilizing a Low Cost Camera as an Inexpensive Barcode Reader,” Sri Lanka Association of Artificial Intelligence Annual Sessions, 2003.
     Google Scholar