QR-DN1.0: A new distorted and noisy QRs dataset

Barcodes are playing a significant role in different industries in the recent years and among the two most popular 2D barcodes, the QR code has grown exponentially. The QR-DN1.0 dataset includes 5 categories of QR codes that will cover low to high density levels. Each group has 15 QR codes: 5 images...

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Autores principales:	Milad Monfared, Abbas Koochari, Radin Monshianmotlagh
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Image reconstruction Image denoising Geometric distortion QR Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/09234dcfa16842aaab7e9fb7c52ca1c4
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id	oai:doaj.org-article:09234dcfa16842aaab7e9fb7c52ca1c4
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spelling	oai:doaj.org-article:09234dcfa16842aaab7e9fb7c52ca1c42021-11-24T04:31:42ZQR-DN1.0: A new distorted and noisy QRs dataset2352-340910.1016/j.dib.2021.107605https://doaj.org/article/09234dcfa16842aaab7e9fb7c52ca1c42021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921008805https://doaj.org/toc/2352-3409Barcodes are playing a significant role in different industries in the recent years and among the two most popular 2D barcodes, the QR code has grown exponentially. The QR-DN1.0 dataset includes 5 categories of QR codes that will cover low to high density levels. Each group has 15 QR codes: 5 images for testing and 10 images for training. After embedding the QRs into 30 color images using blind watermarking techniques and then extracting the QRs from the images taken with the mobile phone camera with three different methods, we will have three groups of 2250 extracted QR images, which provides a total of 6750 distorted and noisy QR images. In each of the mentioned three categories, the data is divided into two parts: testing, with 750 images, and training, with 2250 images. For every distorted QR in the dataset, a non-distorted instance of it is placed as a ground truth. One of the advantages of this data set is that it is real. Because no simulated noise has been added to the images and this dataset is completely derived from the real word challenge of extracting embedded QRs in color images captured from the watermarked image on the screen. It also includes various types of QRs such as single character, short sentence, long sentence, URL and location.Milad MonfaredAbbas KoochariRadin MonshianmotlaghElsevierarticleImage reconstructionImage denoisingGeometric distortionQRComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107605- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Image reconstruction Image denoising Geometric distortion QR Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Image reconstruction Image denoising Geometric distortion QR Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Milad Monfared Abbas Koochari Radin Monshianmotlagh QR-DN1.0: A new distorted and noisy QRs dataset
description	Barcodes are playing a significant role in different industries in the recent years and among the two most popular 2D barcodes, the QR code has grown exponentially. The QR-DN1.0 dataset includes 5 categories of QR codes that will cover low to high density levels. Each group has 15 QR codes: 5 images for testing and 10 images for training. After embedding the QRs into 30 color images using blind watermarking techniques and then extracting the QRs from the images taken with the mobile phone camera with three different methods, we will have three groups of 2250 extracted QR images, which provides a total of 6750 distorted and noisy QR images. In each of the mentioned three categories, the data is divided into two parts: testing, with 750 images, and training, with 2250 images. For every distorted QR in the dataset, a non-distorted instance of it is placed as a ground truth. One of the advantages of this data set is that it is real. Because no simulated noise has been added to the images and this dataset is completely derived from the real word challenge of extracting embedded QRs in color images captured from the watermarked image on the screen. It also includes various types of QRs such as single character, short sentence, long sentence, URL and location.
format	article
author	Milad Monfared Abbas Koochari Radin Monshianmotlagh
author_facet	Milad Monfared Abbas Koochari Radin Monshianmotlagh
author_sort	Milad Monfared
title	QR-DN1.0: A new distorted and noisy QRs dataset
title_short	QR-DN1.0: A new distorted and noisy QRs dataset
title_full	QR-DN1.0: A new distorted and noisy QRs dataset
title_fullStr	QR-DN1.0: A new distorted and noisy QRs dataset
title_full_unstemmed	QR-DN1.0: A new distorted and noisy QRs dataset
title_sort	qr-dn1.0: a new distorted and noisy qrs dataset
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/09234dcfa16842aaab7e9fb7c52ca1c4
work_keys_str_mv	AT miladmonfared qrdn10anewdistortedandnoisyqrsdataset AT abbaskoochari qrdn10anewdistortedandnoisyqrsdataset AT radinmonshianmotlagh qrdn10anewdistortedandnoisyqrsdataset
_version_	1718415957123137536

QR-DN1.0: A new distorted and noisy QRs dataset

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