The Community for Technology Leaders
RSS Icon
Issue No.09 - September (2011 vol.33)
pp: 1834-1843
Orazio Gallo , University of California, Santa Cruz, Santa Cruz
Roberto Manduchi , University of California, Santa Cruz, Santa Cruz
Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications. For instance, they can enable users to access reviews or price comparisons for a product from a picture of its barcode while still in the store. Barcode reading needs to be robust to challenging conditions such as blur, noise, low resolution, or low-quality camera lenses, all of which are extremely common. Surprisingly, even state-of-the-art barcode reading algorithms fail when some of these factors come into play. One reason resides in the early commitment strategy that virtually all existing algorithms adopt: The image is first binarized and then only the binary data are processed. We propose a new approach to barcode decoding that bypasses binarization. Our technique relies on deformable templates and exploits all of the gray-level information of each pixel. Due to our parameterization of these templates, we can efficiently perform maximum likelihood estimation independently on each digit and enforce spatial coherence in a subsequent step. We show by way of experiments on challenging UPC-A barcode images from five different databases that our approach outperforms competing algorithms. Implemented on a Nokia N95 phone, our algorithm can localize and decode a barcode on a VGA image (640 \times 480, JPEG compressed) in an average time of 400-500 ms.
Barcodes, UPC-A, mobile devices, deformable templates.
Orazio Gallo, Roberto Manduchi, "Reading 1D Barcodes with Mobile Phones Using Deformable Templates", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 9, pp. 1834-1843, September 2011, doi:10.1109/TPAMI.2010.229
[1] Redlaser, http:/, Feb. 2010.
[2] UCSC UPC Dataset, , Mar. 2010.
[3] R. Adelmann, M. Langheinrich, and C. Flörkemeier, "A Toolkit for Bar-Code Recognition and Resolving on Camera Phones—Jump Starting the Internet of Things," Proc. Informatik Workshop Mobile and Embedded Interactive Systems, 2006.
[4] D. Chai and F. Hock, "Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras," Proc. Int'l Conf. Information, Comm., and Signal Processing, pp. 1595-1599, 2005.
[5] S. Krešić-Jurić, D. Madej, and F. Santosa, "Applications of Hidden Markov Models in Bar Code Decoding," Pattern Recognition Letters, vol. 27, no. 14, pp. 1665-1672, 2006.
[6] R. Muniz, L. Junco, and A. Otero, "A Robust Software Barcode Reader Using the Hough Transform," Proc. Int'l Conf. Information Intelligence and Systems '99, pp. 313-319, 1999.
[7] E. Ohbuchi, H. Hanaizumi, and L. Hock, "Barcode Readers Using the Camera Device in Mobile Phones," Proc. IEEE Third Int'l Conf. Cyberworlds, pp. 260-265, 2004.
[8] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Trans. Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, Jan. 1979.
[9] T. Pavlidis, J. Swartz, and Y. Wang, "Fundamentals of Bar Code Information Theory," Computer, vol. 23, no. 4, pp. 74-86, 1990.
[10] E. Tekin and J. Coughlan, "A Bayesian Algorithm for Reading 1D Barcodes," Proc. Sixth Canadian Conf. Computer and Robot Vision, 2009.
[11] A. Tropf and D. Chai, "Locating 1-D Bar Codes in DCT-Domain," Proc. IEEE Int'l Conf. Acoustics, Speech and Signal Processing, vol. 2, 2006.
[12] S. Wachenfeld, S. Terlunen, and X. Jiang, "Robust Recognition of 1-D Barcodes Using Camera Phones," Proc. Int'l Conf. Pattern Recognition, pp. 1-4, 2008.
[13] K. Wang, Y. Zou, and H. Wang, "1D Bar Code Reading on Camera Phones," Int'l J. Image and Graphics, vol. 7, no. 3, pp. 529-550, July 2007.
[14] C. Zhang, J. Wang, S. Han, M. Yi, and Z. Zhang, "Automatic Real-Time Barcode Localization in Complex Scenes," Proc. Int'l Conf. Image Processing, pp. 497-500, 2006.
21 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool