Research on QR image code recognition system based on artificial intelligence algorithm

The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Huo Lina, Zhu Jianxing, Singh Pradeep Kumar, Pavlovich Pljonkin Anton
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	artificial intelligence algorithm qr image code image recognition backpropagation neural networks two-dimensional code distortion Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/4e1f0adfb46646aaad70d449d5999b21
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4e1f0adfb46646aaad70d449d5999b21
record_format	dspace
spelling	oai:doaj.org-article:4e1f0adfb46646aaad70d449d5999b212021-12-05T14:10:51ZResearch on QR image code recognition system based on artificial intelligence algorithm2191-026X10.1515/jisys-2020-0143https://doaj.org/article/4e1f0adfb46646aaad70d449d5999b212021-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0143https://doaj.org/toc/2191-026XThe QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.Huo LinaZhu JianxingSingh Pradeep KumarPavlovich Pljonkin AntonDe Gruyterarticleartificial intelligence algorithmqr image codeimage recognitionbackpropagation neural networkstwo-dimensional code distortionScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 855-867 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence algorithm qr image code image recognition backpropagation neural networks two-dimensional code distortion Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	artificial intelligence algorithm qr image code image recognition backpropagation neural networks two-dimensional code distortion Science Q Electronic computers. Computer science QA75.5-76.95 Huo Lina Zhu Jianxing Singh Pradeep Kumar Pavlovich Pljonkin Anton Research on QR image code recognition system based on artificial intelligence algorithm
description	The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.
format	article
author	Huo Lina Zhu Jianxing Singh Pradeep Kumar Pavlovich Pljonkin Anton
author_facet	Huo Lina Zhu Jianxing Singh Pradeep Kumar Pavlovich Pljonkin Anton
author_sort	Huo Lina
title	Research on QR image code recognition system based on artificial intelligence algorithm
title_short	Research on QR image code recognition system based on artificial intelligence algorithm
title_full	Research on QR image code recognition system based on artificial intelligence algorithm
title_fullStr	Research on QR image code recognition system based on artificial intelligence algorithm
title_full_unstemmed	Research on QR image code recognition system based on artificial intelligence algorithm
title_sort	research on qr image code recognition system based on artificial intelligence algorithm
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/4e1f0adfb46646aaad70d449d5999b21
work_keys_str_mv	AT huolina researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm AT zhujianxing researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm AT singhpradeepkumar researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm AT pavlovichpljonkinanton researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm
_version_	1718371665650384896

Research on QR image code recognition system based on artificial intelligence algorithm

Ejemplares similares