Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved]
Background: Manufacturing markings printed on products play an important role in the handling and use of pharmaceuticals and perishable foods. Currently, optical character recognition, neural networks, deep learning-based methods, and combinations of these methods are used to recognize these codes....
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2022-09-01
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| Series: | F1000Research |
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| Online Access: | https://f1000research.com/articles/11-1099/v1 |
| _version_ | 1797738978551529472 |
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| author | Pekka Toivanen Jarmo Koponen Keijo Haataja |
| author_facet | Pekka Toivanen Jarmo Koponen Keijo Haataja |
| author_sort | Pekka Toivanen |
| collection | DOAJ |
| description | Background: Manufacturing markings printed on products play an important role in the handling and use of pharmaceuticals and perishable foods. Currently, optical character recognition, neural networks, deep learning-based methods, and combinations of these methods are used to recognize these codes. Methods: This systematic review was performed to find papers that can answer the following research questions: How have machine vision methods that can recognize product texts evolved over the past eight years? What are the most common difficulties in recognizing product texts? Articles published between 2012 and 2020 were systematically searched from Science Direct/SCOPUS, and Google Scholar in November-December 2020. Ten studies were eligible, with inclusion criteria: details about the recognition method used, performance analysis result, imaging method, product and the text printed on it. Results: Product text recognition methods have evolved significantly over the last two years to tolerate the most common difficulties in the field. This is due to the ability of the deep learning neural network (DNN) architectures such as convolutional neural networks (CNN) to extract and learn salient character features straight from packaging surface images. Four of the most recent methods use two consecutive deep learning networks, one detecting the text area based on an image captured from the product package's surface and the other recognizing the characters within. Furthermore, this paper presents solutions to the most common product text recognition difficulties. Conclusions: There were a limited number of studies that met the eligibility criteria for this systematic review. The study's aim was to evaluate the development of machine vision methods for recognizing manufacturing marking texts printed on the surface of products. The study results demonstrated how methods have evolved over time, beginning with optical character recognition, and advancing to methods which can recognize texts despite the field's most common problems. |
| first_indexed | 2024-03-12T13:51:37Z |
| format | Article |
| id | doaj.art-18544821f5174ece9c5284d0062f2548 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-03-12T13:51:37Z |
| publishDate | 2022-09-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-18544821f5174ece9c5284d0062f25482023-08-23T00:00:00ZengF1000 Research LtdF1000Research2046-14022022-09-0111137029Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved]Pekka Toivanen0Jarmo Koponen1https://orcid.org/0000-0002-8963-9315Keijo Haataja2School of Computing, Kuopio campus, University of Eastern Finland, Kuopio, Pohjois-Savo, FI-70211, FinlandSchool of Computing, Kuopio campus, University of Eastern Finland, Kuopio, Pohjois-Savo, FI-70211, FinlandSchool of Computing, Kuopio campus, University of Eastern Finland, Kuopio, Pohjois-Savo, FI-70211, FinlandBackground: Manufacturing markings printed on products play an important role in the handling and use of pharmaceuticals and perishable foods. Currently, optical character recognition, neural networks, deep learning-based methods, and combinations of these methods are used to recognize these codes. Methods: This systematic review was performed to find papers that can answer the following research questions: How have machine vision methods that can recognize product texts evolved over the past eight years? What are the most common difficulties in recognizing product texts? Articles published between 2012 and 2020 were systematically searched from Science Direct/SCOPUS, and Google Scholar in November-December 2020. Ten studies were eligible, with inclusion criteria: details about the recognition method used, performance analysis result, imaging method, product and the text printed on it. Results: Product text recognition methods have evolved significantly over the last two years to tolerate the most common difficulties in the field. This is due to the ability of the deep learning neural network (DNN) architectures such as convolutional neural networks (CNN) to extract and learn salient character features straight from packaging surface images. Four of the most recent methods use two consecutive deep learning networks, one detecting the text area based on an image captured from the product package's surface and the other recognizing the characters within. Furthermore, this paper presents solutions to the most common product text recognition difficulties. Conclusions: There were a limited number of studies that met the eligibility criteria for this systematic review. The study's aim was to evaluate the development of machine vision methods for recognizing manufacturing marking texts printed on the surface of products. The study results demonstrated how methods have evolved over time, beginning with optical character recognition, and advancing to methods which can recognize texts despite the field's most common problems.https://f1000research.com/articles/11-1099/v1Machine Vision Imaging System Character Recognition OCR Deep Learning Producteng |
| spellingShingle | Pekka Toivanen Jarmo Koponen Keijo Haataja Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved] F1000Research Machine Vision Imaging System Character Recognition OCR Deep Learning Product eng |
| title | Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved] |
| title_full | Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved] |
| title_fullStr | Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved] |
| title_full_unstemmed | Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved] |
| title_short | Recent advancements in machine vision methods for product code recognition: A systematic review [version 1; peer review: 2 approved] |
| title_sort | recent advancements in machine vision methods for product code recognition a systematic review version 1 peer review 2 approved |
| topic | Machine Vision Imaging System Character Recognition OCR Deep Learning Product eng |
| url | https://f1000research.com/articles/11-1099/v1 |
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