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