Deep learning for classification of noisy QR codes
Rebecca Leygonie Sylvain Lobry ) Laurent Wendling (LIPADE)
We wish to define the limits of a classical classification model based on
deep learning when applied to abstract images, which do not represent visually
identifiable objects.QR codes (Quick Response codes) fall into this category of
abstract images: one bit corresponding to one encoded character, QR codes were
not designed to be decoded manually. To understand the limitations of a deep
learning-based model for abstract image classification, we train an image
classification model on QR codes generated from information obtained when
reading a health pass. We compare a classification model with a classical
(deterministic) decoding method in the presence of noise. This study allows us
to conclude that a model based on deep learning can be relevant for the
understanding of abstract images.