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.

我们旨在研究基于深度学习的经典分类模型在应用于抽象图像（不代表可视可识别物体）时的界限。为了了解基于深度学习的模型在抽象图像分类中的限制，我们使用从身份证阅读获得的信息生成二维码，并训练一个图像分类模型。我们将分类模型与传统的（确定性）解码方法在噪声存在下进行比较。研究结果表明，基于深度学习的模型在理解抽象图像方面具有相关性。

深度学习用于噪声二维码的分类

Deep learning for classification of noisy QR codes

Much computer vision research has focused on natural images, but technical
documents typically consist of abstract images, such as charts, drawings,
diagrams, and schematics. How well do general web search engines discover
abstract images? Recent advancements in computer vision and machine learning
have led to the rise of reverse image search engines. Where conventional search
engines accept a text query and return a set of document results, including
images, a reverse image search accepts an image as a query and returns a set of
images as results. This paper evaluates how well common reverse image search
engines discover abstract images. We conducted an experiment leveraging images
from Wikimedia Commons, a website known to be well indexed by Baidu, Bing,
Google, and Yandex. We measure how difficult an image is to find again
(retrievability), what percentage of images returned are relevant (precision),
and the average number of results a visitor must review before finding the
submitted image (mean reciprocal rank). When trying to discover the same image
again among similar images, Yandex performs best. When searching for pages
containing a specific image, Google and Yandex outperform the others when
discovering photographs with precision scores ranging from 0.8191 to 0.8297,
respectively. In both of these cases, Google and Yandex perform better with
natural images than with abstract ones achieving a difference in retrievability
as high as 54\% between images in these categories. These results affect anyone
applying common web search engines to search for technical documents that use
abstract images.

本文评估常见反向图像搜索引擎在发现技术文档中抽象图像方面的表现，发现自然图像上，Yandex 表现更好，在搜索包含特定图像的页面时，Google 和 Yandex 的精度得分约为 0.8191 至 0.8297，且在这两种情况下，Google 和 Yandex 在自然图像上的表现优于抽象图像，这些结果影响着使用常见网络搜索引擎搜索使用抽象图像的技术文档的人们