Improving the module recognition rate of high density QR codes (Version 40) by using centrality bias

Iuliia Tkachenko 1 William Puech 1 Olivier Strauss 1 Jean-Marc Gaudin 2 Christophe Destruel 2 Christian Guichard 2
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 AI - Authentication Industries [Montpellier]
Abstract : The number of real world applications of Quick Response (QR) codes is increasing, along with the extent of stored information in QR codes. That is why high density QR codes are commonly encountered in daily life. Unfortunately, high density versions of QR codes are not very well readable by most smartphone QR code readers. The main reading problems are QR code tilt correction, binarization and module recognition. Quite often the applications cannot determine the QR code version due to tilt correction problems. Binarization algorithms use global threshold methods, that are sensitive to print-and-scan distortion and luminosity. Here we propose to use the centrality bias of each module to improve the recognition of black and white modules in high density QR codes. The proposed method increases the recognition rate of high density QR codes, as confirmed by the experimental results.
Keywords : bar codes QR code tilt correction binarization algorithms black modules centrality bias global threshold methods high density QR codes luminosity module recognition module recognition rate print-andscan distortion quick response codes smartphone QR code readers white modules Decoding Error correction codes Image recognition Image restoration Mathematical model Optical wavelength conversion Standards QR code module centrality bias weighted mean squared error
Type de document :
Communication dans un congrès
IPTA: Image Processing Theory, Tools and Applications, Oct 2014, Paris, France. 4th International Conference on Image Processing Theory, Tools and Applications, 2014, 〈http://ipta14.ibisc.univ-evry.fr/〉. 〈10.1109/IPTA.2014.7001950〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01379590
Contributeur : William Puech <>
Soumis le : mardi 11 octobre 2016 - 17:17:34
Dernière modification le : mardi 16 octobre 2018 - 07:52:01

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LIRMM | ICARLIRMM | MIPS | UNIV-MONTPELLIER

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Iuliia Tkachenko, William Puech, Olivier Strauss, Jean-Marc Gaudin, Christophe Destruel, et al.. Improving the module recognition rate of high density QR codes (Version 40) by using centrality bias. IPTA: Image Processing Theory, Tools and Applications, Oct 2014, Paris, France. 4th International Conference on Image Processing Theory, Tools and Applications, 2014, 〈http://ipta14.ibisc.univ-evry.fr/〉. 〈10.1109/IPTA.2014.7001950〉. 〈lirmm-01379590〉

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