{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T20:02:01Z","timestamp":1766088121353,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T00:00:00Z","timestamp":1694995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"M\u0160VVa\u0160 SR","award":["KEGA 006STU-4\/2021"],"award-info":[{"award-number":["KEGA 006STU-4\/2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition. Our comparative study deals with four types of artificial neural networks\u2014multilayer perceptrons, probabilistic neural networks, radial basis function neural networks, and convolutional neural networks\u2014and investigates their ability to classify 2D matrix codes (Data Matrix codes, QR codes, and Aztec codes) as well as their rotation. The paper presents the basic building blocks of these artificial neural networks and their architecture and compares the classification accuracy of 2D matrix codes under different configurations of these neural networks. A dataset of 3000 synthetic code samples was used to train and test the neural networks. When the neural networks were trained on the full dataset, the convolutional neural network showed its superiority, followed by the RBF neural network and the multilayer perceptron.<\/jats:p>","DOI":"10.3390\/jimaging9090188","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T02:26:17Z","timestamp":1695090377000},"page":"188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using Different Types of Artificial Neural Networks to Classify 2D Matrix Codes and Their Rotations\u2014A Comparative Study"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5982-6806","authenticated-orcid":false,"given":"Ladislav","family":"Karrach","sequence":"first","affiliation":[{"name":"Department of Manufacturing and Automation Technology, Faculty of Technology, Technical University in Zvolen, Masarykova 24, 960 01 Zvolen, Slovakia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6676-8245","authenticated-orcid":false,"given":"Elena","family":"Pivar\u010diov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Manufacturing and Automation Technology, Faculty of Technology, Technical University in Zvolen, Masarykova 24, 960 01 Zvolen, Slovakia"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"unstructured":"Karrach, L., and Pivar\u010diov\u00e1, E. 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