Title :
Improving the module recognition rate of high density QR codes (Version 40) by using centrality bias
Author :
Tkachenko, Iuliia ; Puech, William ; Strauss, Olivier ; Gaudin, Jean-Marc ; Destruel, Christophe ; Guichard, Christian
Author_Institution :
LIRMM Lab., Univ. of Montpellier 2, Montpellier, France
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; image recognition; 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; module recognition; weighted mean squared error;
Conference_Titel :
Image Processing Theory, Tools and Applications (IPTA), 2014 4th International Conference on
Conference_Location :
Paris
Print_ISBN :
978-1-4799-6462-8
DOI :
10.1109/IPTA.2014.7001950
