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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2021-07-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2020-0143 |
| _version_ | 1828123741833396224 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T14:57:04Z |
| format | Article |
| id | doaj.art-4e1f0adfb46646aaad70d449d5999b21 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-11T14:57:04Z |
| publishDate | 2021-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-4e1f0adfb46646aaad70d449d5999b212022-12-22T04:17:11ZengDe GruyterJournal of Intelligent Systems2191-026X2021-07-0130185586710.1515/jisys-2020-0143Research on QR image code recognition system based on artificial intelligence algorithmHuo Lina0Zhu Jianxing1Singh Pradeep Kumar2Pavlovich Pljonkin Anton3College of Mathematics and Information Technology, XingTai University, XingTai 054001, ChinaCollege of Mathematics and Information Technology, XingTai University, XingTai 054001, ChinaKIET Group of Institutions, Delhi-NCR, Ghaziabad, UP, IndiaInstitute of Computer Technologies and Information Security, Southern Federal University, RussiaThe 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.https://doi.org/10.1515/jisys-2020-0143artificial intelligence algorithmqr image codeimage recognitionbackpropagation neural networkstwo-dimensional code distortion |
| spellingShingle | Huo Lina Zhu Jianxing Singh Pradeep Kumar Pavlovich Pljonkin Anton Research on QR image code recognition system based on artificial intelligence algorithm Journal of Intelligent Systems artificial intelligence algorithm qr image code image recognition backpropagation neural networks two-dimensional code distortion |
| title | 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_short | 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 |
| topic | artificial intelligence algorithm qr image code image recognition backpropagation neural networks two-dimensional code distortion |
| url | https://doi.org/10.1515/jisys-2020-0143 |
| work_keys_str_mv | AT huolina researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm AT zhujianxing researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm AT singhpradeepkumar researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm AT pavlovichpljonkinanton researchonqrimagecoderecognitionsystembasedonartificialintelligencealgorithm |