Deep learning for classification of noisy QR codes

• URL: http://arxiv.org/abs/2307.10677v1
• Date: Thu, 20 Jul 2023 07:57:14 GMT
• Title: Deep learning for classification of noisy QR codes
• Authors: Rebecca Leygonie (LIPADE), Sylvain Lobry (LIPADE)), Laurent Wendling (LIPADE)
• Abstract summary: We train an image classification model on QR codes generated from information obtained when reading a health pass. We conclude that a model based on deep learning can be relevant for the understanding of abstract images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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