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Multiple attention-based encoder–decoder networks for gas meter character recognition

Factories swiftly and precisely grasp the real-time data of the production instrumentation, which is the foundation for the development and progress of industrial intelligence in industrial production. Weather, light, angle, and other unknown circumstances, on the other hand, impair the image qualit...

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Autores principales: Li, Weidong, Wang, Shuai, Ullah, Inam, Zhang, Xuehai, Duan, Jinlong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209494/
https://www.ncbi.nlm.nih.gov/pubmed/35725753
http://dx.doi.org/10.1038/s41598-022-14434-0
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author Li, Weidong
Wang, Shuai
Ullah, Inam
Zhang, Xuehai
Duan, Jinlong
author_facet Li, Weidong
Wang, Shuai
Ullah, Inam
Zhang, Xuehai
Duan, Jinlong
author_sort Li, Weidong
collection PubMed
description Factories swiftly and precisely grasp the real-time data of the production instrumentation, which is the foundation for the development and progress of industrial intelligence in industrial production. Weather, light, angle, and other unknown circumstances, on the other hand, impair the image quality of meter dials in natural environments, resulting in poor dial image quality. The remote meter reading system has trouble recognizing dial pictures in extreme settings, challenging it to meet industrial production demands. This paper provides multiple attention and encoder–decoder-based gas meter recognition networks (MAEDR) for this problem. First, from the acquired dial photos, the dial images with extreme conditions such as overexposure, artifacts, blurring, incomplete display of characters, and occlusion are chosen to generate the gas meter dataset. Then, a new character recognition network is proposed utilizing multiple attention and an encoder–decoder structure. Convolutional neural networks (CNN) extract visual features from dial images, encode visual features employing multi-head self-attention and position information, and facilitate feature alignment using the connectionist temporal classification (CTC) method. A novel two-step attention decoder is presented to improve the accuracy of recognition results. convolutional block attention module (CBAM) reweights the visual features from the CNN and the semantic features computed by the encoder to improve model performance; long short-term memory attention (LSTM attention) focuses on the relationship between feature sequences. According to experimental data, our system can effectively and efficiently identify industrial gas meter picture digits with 91.1% identification accuracy, faster inference speed, and higher accuracy than standard algorithms. The accuracy and practicality of the recognition can fulfill the needs of instrument data detection and recognition in industrial production, and it has a wide range of applications.
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spelling pubmed-92094942022-06-22 Multiple attention-based encoder–decoder networks for gas meter character recognition Li, Weidong Wang, Shuai Ullah, Inam Zhang, Xuehai Duan, Jinlong Sci Rep Article Factories swiftly and precisely grasp the real-time data of the production instrumentation, which is the foundation for the development and progress of industrial intelligence in industrial production. Weather, light, angle, and other unknown circumstances, on the other hand, impair the image quality of meter dials in natural environments, resulting in poor dial image quality. The remote meter reading system has trouble recognizing dial pictures in extreme settings, challenging it to meet industrial production demands. This paper provides multiple attention and encoder–decoder-based gas meter recognition networks (MAEDR) for this problem. First, from the acquired dial photos, the dial images with extreme conditions such as overexposure, artifacts, blurring, incomplete display of characters, and occlusion are chosen to generate the gas meter dataset. Then, a new character recognition network is proposed utilizing multiple attention and an encoder–decoder structure. Convolutional neural networks (CNN) extract visual features from dial images, encode visual features employing multi-head self-attention and position information, and facilitate feature alignment using the connectionist temporal classification (CTC) method. A novel two-step attention decoder is presented to improve the accuracy of recognition results. convolutional block attention module (CBAM) reweights the visual features from the CNN and the semantic features computed by the encoder to improve model performance; long short-term memory attention (LSTM attention) focuses on the relationship between feature sequences. According to experimental data, our system can effectively and efficiently identify industrial gas meter picture digits with 91.1% identification accuracy, faster inference speed, and higher accuracy than standard algorithms. The accuracy and practicality of the recognition can fulfill the needs of instrument data detection and recognition in industrial production, and it has a wide range of applications. Nature Publishing Group UK 2022-06-20 /pmc/articles/PMC9209494/ /pubmed/35725753 http://dx.doi.org/10.1038/s41598-022-14434-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Weidong
Wang, Shuai
Ullah, Inam
Zhang, Xuehai
Duan, Jinlong
Multiple attention-based encoder–decoder networks for gas meter character recognition
title Multiple attention-based encoder–decoder networks for gas meter character recognition
title_full Multiple attention-based encoder–decoder networks for gas meter character recognition
title_fullStr Multiple attention-based encoder–decoder networks for gas meter character recognition
title_full_unstemmed Multiple attention-based encoder–decoder networks for gas meter character recognition
title_short Multiple attention-based encoder–decoder networks for gas meter character recognition
title_sort multiple attention-based encoder–decoder networks for gas meter character recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209494/
https://www.ncbi.nlm.nih.gov/pubmed/35725753
http://dx.doi.org/10.1038/s41598-022-14434-0
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