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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 33, Number 11(3) (2021)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 3983-3990
S&M2739 Research Paper of Special Issue https://doi.org/10.18494/SAM.2021.3518 Published: November 30, 2021 Recognition of Vehicle License Plate Based on Hopfield Artificial Neural Network [PDF] Tian-Syung Lan, Jiawei Li, Xuan-Jun Dai, Ho-Sheng Chen, and Ruiming Liu (Received July 1, 2021; Accepted October 7, 2021) Keywords: Hopfield, general license plate number recognition, image recognition
Vehicle license plate recognition has become widely used to control and efficiently manage transportation systems. The increasing number of vehicles and worsening congestion on the road make the accuracy of the traditional recognition system low. Thus, introducing a neural network in license plate recognition has been paid considerable attention. In this paper, we aim to enhance recognition accuracy by using the Hopfield artificial neural network (ANN). Test results showed that the recognition method based on the Hopfield ANN is found to be efficient and accurate.
Corresponding author: Xuan-Jun Dai  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Tian-Syung Lan, Jiawei Li, Xuan-Jun Dai, Ho-Sheng Chen, and Ruiming Liu, Recognition of Vehicle License Plate Based on Hopfield Artificial Neural Network, Sens. Mater., Vol. 33, No. 11, 2021, p. 3983-3990. |
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Hirotaka Takizawa, Masatoshi Minamisawa, Hirohiko Motoki, Koichiro Kuwahara, and Masaki Yamaguchi
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