Deep learning for classification of noisy QR codes
Abstract
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.
Cite
@article{arxiv.2307.10677,
title = {Deep learning for classification of noisy QR codes},
author = {Rebecca Leygonie and Sylvain Lobry and ) and Laurent Wendling (LIPADE)},
journal= {arXiv preprint arXiv:2307.10677},
year = {2023}
}
Comments
in French language. RFIAP 2022 - Reconnaissance des Formes, Image, Apprentissage et Perception, Jul 2022, Vannes (Bretagne), France