Improving the module recognition rate of high density QR codes (Version 40) by using centrality bias - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2014

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

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.
No file

Dates and versions

lirmm-01379590 , version 1 (11-10-2016)

Identifiers

Cite

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. ⟨10.1109/IPTA.2014.7001950⟩. ⟨lirmm-01379590⟩
133 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More