# Deep learning for classification of noisy QR codes

Source: [Deep learning for classification of noisy QR codes](<https://www.sota2.com/research/paper/68fa70181e209131cea48ec7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:33:16.495Z

Authors: Rebecca Leygonie, Sylvain Lobry (LIPADE)), Laurent Wendling

Published: 2023-07-20T07:57:14.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.10677>)

[PDF](<https://arxiv.org/pdf/2307.10677.pdf>)