{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:19:20Z","timestamp":1778170760968,"version":"3.51.4"},"reference-count":39,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1515\/jisys-2020-0143","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T17:07:18Z","timestamp":1626455238000},"page":"855-867","source":"Crossref","is-referenced-by-count":31,"title":["Research on QR image code recognition system based on artificial intelligence algorithm"],"prefix":"10.1515","volume":"30","author":[{"given":"Lina","family":"Huo","sequence":"first","affiliation":[{"name":"College of Mathematics and Information Technology, XingTai University , XingTai 054001 , China"}]},{"given":"Jianxing","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Information Technology, XingTai University , XingTai 054001 , China"}]},{"given":"Pradeep Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"KIET Group of Institutions, Delhi-NCR , Ghaziabad , UP , India"}]},{"given":"Pljonkin Anton","family":"Pavlovich","sequence":"additional","affiliation":[{"name":"Institute of Computer Technologies and Information Security , Southern Federal University , Russia"}]}],"member":"374","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"2025120523322278634_j_jisys-2020-0143_ref_001","doi-asserted-by":"crossref","unstructured":"Hongyu L, Hui C, Ying W, Yong C, Wei Y. Prediction of two-dimensional topography of laser cladding based on neural network. Int J Mod Phys B. 2019;33:1940034.","DOI":"10.1142\/S0217979219400344"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_002","unstructured":"Rathee G, Sharma A, Saini H, Kumar R, Iqbal R. A hybrid framework for multimedia data processing in IoT-healthcare using blockchain technology. Multimed Tools Appl. 2019;19:1\u201323."},{"key":"2025120523322278634_j_jisys-2020-0143_ref_003","doi-asserted-by":"crossref","unstructured":"Rathee G, Sharma A, Kumar R, Iqbal R. A secure communicating things network framework for industrial IoT using blockchain technology. Ad Hoc Netw. 2019;94:101933.","DOI":"10.1016\/j.adhoc.2019.101933"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_004","doi-asserted-by":"crossref","unstructured":"Frankovsk\u00fd P, P\u00e1stor M, Dominik L, Kicko M, Trebu\u0148a P, Hroncov\u00e1 D, et al. Wheeled mobile robot in structured environment. In 2018 ELEKTRO. IEEE; 2018 May. p. 1\u20135.","DOI":"10.1109\/ELEKTRO.2018.8398375"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_005","doi-asserted-by":"crossref","unstructured":"Bo\u017eek P, Bez\u00e1k P, Nikitin Y, Fedorko G, Fabian M. Increasing the production system productivity using inertial navigation. Manuf Technol. 2015;15:274\u20138.","DOI":"10.21062\/ujep\/x.2015\/a\/1213-2489\/MT\/15\/3\/274"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_006","unstructured":"Shettar IM. Quick response (QR) codes in libraries: case study on the use of QR codes in the central library, NITK. Proc. TIFR-BOSLA National Conference on Future Librarianship. Mumbai: Imperial Publications;  2016. p. 129\u201334."},{"key":"2025120523322278634_j_jisys-2020-0143_ref_007","doi-asserted-by":"crossref","unstructured":"Karrach L, Pivar\u010diov\u00e1 E, Bo\u017eek P. Identification of QR code perspective distortion based on edge directions and edge projections analysis. J Imaging. 2020;6(7):67.","DOI":"10.3390\/jimaging6070067"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_008","doi-asserted-by":"crossref","unstructured":"Sharma A, Ansari MD, Kumar R. A comparative study of edge detectors in digital image processing. 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). Solan, India: IEEE; 2017 Sept. p. 246\u201350.","DOI":"10.1109\/ISPCC.2017.8269683"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_009","doi-asserted-by":"crossref","unstructured":"Rathee G, Sharma A, Kumar R, Ahmad F, Iqbal R. A trust management scheme to secure mobile information centric networks. Comput Commun. 2020;151:66\u201375.","DOI":"10.1016\/j.comcom.2019.12.024"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_010","doi-asserted-by":"crossref","unstructured":"Sharma A, Kumar R. Computation of the reliable and quickest data path for healthcare services by using service-level agreements and energy constraints. Arab J Sci Eng. 2019;44(11):9087\u2013104.","DOI":"10.1007\/s13369-019-03836-4"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_011","doi-asserted-by":"crossref","unstructured":"Bithas PS, Michailidis ET, Nomikos N, Vouyioukas D, Kanatas AG. A survey on machine-learning techniques for UAV-based communications. Sensors. 2019;19(23):5170.","DOI":"10.3390\/s19235170"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_012","doi-asserted-by":"crossref","unstructured":"Xu T, Zhang H, Liu G, Zhao H, Zhang E, Zhang C. Two-dimensional code recognition algorithm based on neural network. Comput Sci Appl. 2018;8(10):1552\u20137.","DOI":"10.12677\/CSA.2018.810169"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_013","doi-asserted-by":"crossref","unstructured":"Gan Z, Fang J, Guan H, Tang J, Chen Z. Research and application of binarization algorithm of qr code image under complex illumination. J Appl Opt. 2018;39(5):667\u201373.","DOI":"10.5768\/JAO201839.0502002"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_014","unstructured":"Jiang S, Wu W. inventors; Fujian Landi Commercial Equipment Co Ltd, assignee. Method and system for decoding two-dimensional code using weighted average gray-sclae algorithm.United States patent US 10,108,835; 2018."},{"key":"2025120523322278634_j_jisys-2020-0143_ref_015","doi-asserted-by":"crossref","unstructured":"Vera E, Lucio D, Fernandes LAF, Velho L. Hough transform for real-time plane detection in depth images. Pattern Recognit Lett. 2018;103(FEB.1):8\u201315.","DOI":"10.1016\/j.patrec.2017.12.027"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_016","doi-asserted-by":"crossref","unstructured":"Karrach L, Pivar\u010diov\u00e1 E, Bo\u017eek P. Identification of QR code perspective distortion based on edge directions and edge projections analysis. J Imag. 2020;6(7):67.","DOI":"10.3390\/jimaging6070067"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_017","doi-asserted-by":"crossref","unstructured":"Li S, Shang J, Duan Z, Huang J. Fast detection method of quick response code based on run-length coding. IET Image Process. 2017;12(4):546\u201351.","DOI":"10.1049\/iet-ipr.2017.0677"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_018","doi-asserted-by":"crossref","unstructured":"Belussi LF, Hirata NS. Fast component-based QR code detection in arbitrarily acquired images. J Math Imag Vis. 2013;45(3):277\u201392.","DOI":"10.1007\/s10851-012-0355-x"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_019","doi-asserted-by":"crossref","unstructured":"Bodn\u00e1r P, Ny\u00fal L. Improved QR code localization using boosted cascade of weak classifiers. Acta Cyber (Szeged). 2015;22(1):21\u201333.","DOI":"10.14232\/actacyb.22.1.2015.3"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_020","doi-asserted-by":"crossref","unstructured":"Tribak H, Zaz Y. QR code recognition based on principal components analysis method. (IJACSA) Int J Adv Comput Sci Appl. 2017;8(4):698\u2013708.","DOI":"10.14569\/IJACSA.2017.080433"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_021","doi-asserted-by":"crossref","unstructured":"Tribak H, Zaz Y. QR code patterns localization based on Hu invariant moments. Int J Adv Comput Sci Appl. 2017;8(9):162\u201372.","DOI":"10.14569\/IJACSA.2017.080924"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_022","doi-asserted-by":"crossref","unstructured":"Ci\u0105\u017cy\u0144ski K, Fabija\u0144ska A. Detection of QR-codes in digital images based on histogram similarity. Image Process Commun. 2015;20(2):41\u20138.","DOI":"10.1515\/ipc-2015-0033"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_023","doi-asserted-by":"crossref","unstructured":"Szentandr\u00e1si I, Herout A, Dubsk\u00e1 M. Fast detection and recognition of QR codes in high-resolution images. Proceedings of the 28th Spring Conference on Computer Graphics. New York, US: Association for Computing Machinery;  2012 May. p. 129\u201336.","DOI":"10.1145\/2448531.2448548"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_024","unstructured":"Gaur P, Tiwari S. Recognition of 2D barcode images using edge detection and morphological operation. Int J Comput Sci Mob Comput. 2014;3(4):1277\u201382."},{"key":"2025120523322278634_j_jisys-2020-0143_ref_025","doi-asserted-by":"crossref","unstructured":"Suran K. QR code image correction based on corner detection and convex hull algorithm. J Multimed. 2013;8(6):662.","DOI":"10.4304\/jmm.8.6.662-668"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_026","doi-asserted-by":"crossref","unstructured":"Sun A, Sun Y, Liu C. The QR-code reorganization in illegible snapshots taken by mobile phones. 2007 International Conference on Computational Science and its Applications (ICCSA 2007). Kuala Lumpur, Malaysia: IEEE; 2007 Aug. p. 532\u20138.","DOI":"10.1109\/ICCSA.2007.86"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_027","doi-asserted-by":"crossref","unstructured":"Hansen DK, Nasrollahi K, Rasmussen CB, Moeslund TB. Real-time barcode detection and classification using deep learning. IJCCI. 2017;1:321\u20137.","DOI":"10.5220\/0006508203210327"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_028","doi-asserted-by":"crossref","unstructured":"Zharkov A, Zagaynov I. Universal barcode detector via semantic segmentation. 2019 International Conference on Document Analysis and Recognition (ICDAR). Sydney, Australia: IEEE; 2019 Sept. p. 837\u201343.","DOI":"10.1109\/ICDAR.2019.00139"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_029","doi-asserted-by":"crossref","unstructured":"Chou TH, Ho CS, Kuo YF. QR code detection using convolutional neural networks. 2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS). Taipei, Taiwan: IEEE; 2015 May. p. 1\u20135.","DOI":"10.1109\/ARIS.2015.7158354"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_030","doi-asserted-by":"crossref","unstructured":"Kurniawan WC, Okumura H, Handayani AN. An improvement on qr code limit angle detection using convolution neural network. 2019 International Conference on Electrical, Electronics and Information Engineering (ICEEIE). Vol. 6. Denpasar, Bali, Indonesia:  IEEE; 2019 Oct. p. 234\u20138.","DOI":"10.1109\/ICEEIE47180.2019.8981449"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_031","unstructured":"Chen YW, Peng DG, Fei XI, Qian Y. Infrared image recognition based on region growing method and bp neural network [J]. Laser Infrared. 2018;48(3):1\u20138."},{"key":"2025120523322278634_j_jisys-2020-0143_ref_032","doi-asserted-by":"crossref","unstructured":"De Li XG, Sun Y, Cui L. Research on anti-counterfeiting technology based on QR code image watermarking algorithm. Int J Multimed Ubiquitous Eng. 2017;12(5):57\u201366.","DOI":"10.14257\/ijmue.2017.12.5.05"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_033","unstructured":"Xuhuang C, Gaoping S, Yang W. Parallel matched filtering algorithm with low complexity. Computing, Performance and Communication Systems.  2016;1(1):17--21."},{"key":"2025120523322278634_j_jisys-2020-0143_ref_034","doi-asserted-by":"crossref","unstructured":"Chen L, Zhang F, Sun L. Research on the calibration of binocular camera based on bp neural network optimized by improved genetic simulated annealing algorithm. IEEE Access. 2020;PP(99):1.","DOI":"10.1109\/ACCESS.2020.2992652"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_035","doi-asserted-by":"crossref","unstructured":"Tiwari A, Singh PK, Amin S. A survey on shadow detection and removal in images and video sequences. 2016 6th International Conference \u2013 Cloud System and Big Data Engineering (Confluence). Piscataway, New Jersey: Noida; 2016. p. 518\u201323. 10.1109\/CONFLUENCE.2016.7508175.","DOI":"10.1109\/CONFLUENCE.2016.7508175"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_036","doi-asserted-by":"crossref","unstructured":"Kaur G, Bhardwaj N, Singh PK. An analytic review on image enhancement techniques based on soft computing approach. In: Urooj S, Virmani J, editors. Sensors and image processing. advances in intelligent systems and computing. Vol 651. Singapore: Springer; 2018. 10.1007\/978-981-10-6614-6_26.","DOI":"10.1007\/978-981-10-6614-6_26"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_037","doi-asserted-by":"crossref","unstructured":"Bhardwaj N, Kaur G, Singh PK. A systematic review on image enhancement techniques. In: Urooj S, Virmani J, editors. Sensors and image processing. advances in intelligent systems and computing. Vol 651. Singapore: Springer; 2018. 10.1007\/978-981-10-6614-6_23.","DOI":"10.1007\/978-981-10-6614-6_23"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_038","doi-asserted-by":"crossref","unstructured":"Ajay A, Singh PK. Novel digital image water marking technique against geometric attacks. Int J Mod Educ Comput Sci. 2015;7(8):61\u20138.","DOI":"10.5815\/ijmecs.2015.08.07"},{"key":"2025120523322278634_j_jisys-2020-0143_ref_039","doi-asserted-by":"crossref","unstructured":"Agarwal N, Singh AK, Singh PK. Survey of robust and imperceptible watermarking. Multimed Tools Appl. 2019;78:8603\u201333. 10.1007\/s11042-018-7128-5.","DOI":"10.1007\/s11042-018-7128-5"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0143\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:33:38Z","timestamp":1764977618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2020-0143\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,9,22]]},"published-print":{"date-parts":[[2021,9,22]]}},"alternative-id":["10.1515\/jisys-2020-0143"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2020-0143","relation":{},"ISSN":["2191-026X"],"issn-type":[{"value":"2191-026X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}