A dataset of QR Codes on top of different surfaces (flat and challenging surfaces)

Published: 13 November 2023| Version 1 | DOI: 10.17632/m6mfwc52vk.1
Ismael Benito-Altamirano,


This comprehensive dataset consists of a diverse collection of images, primarily focusing on QR codes placed on different surfaces. The dataset is meticulously organized into three main categories: "flat," "random," and "synthetic_small." The "flat" category encompasses images of QR codes positioned exclusively on planar surfaces, providing a foundational dataset for various computer vision tasks. In contrast, the "random" category presents a more challenging dataset, featuring QR codes placed on surfaces with varying orientations and complexities. Lastly, the "synthetic_small" category focuses on synthetic images with QR codes on planar surfaces, enhancing the dataset's depth and applicability. Annotations for the dataset are meticulously provided in JSON format, furnishing essential metadata for each image. These annotations are invaluable for tasks such as image classification, analysis, and, notably, object detection of QR codes. With its wide-ranging content and meticulously structured data, this dataset serves as a vital resource for researchers and practitioners in the field of computer vision. It enables exploration and development of algorithms across various domains, offering a diverse set of challenges and opportunities for advancing computer vision capabilities in the context of QR code recognition on different surfaces.



Universitat de Barcelona


Computer Vision, Spline Model, Feature Extraction, Fitting Curve