Rezaei, M orcid.org/0000-0003-3892-421X and Isehaghi, M (2019) An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network. In: 2018 9th Conference on Artificial Intelligence and Robotics and 2nd Asia-Pacific International Symposium. 2018 9th Conference on Artificial Intelligence and Robotics and 2nd Asia-Pacific International Symposium, 10 Dec 2018, Kish Island, Iran. IEEE , pp. 7-13. ISBN 978-1-7281-2842-9
Abstract
Accurate license plate localization is the most important prerequisite in ANPR (Automatic Number Plate Recognition) systems. Majority of the existing algorithms use a single feature to obtain the license plate location which causes to potential false detections. In this article we propose a robust methodology using 16 statistical features while we still preserve real-time processing of the system which is a requirement for such applications. The proposed method uses a Vertical Projection technique and Discrete Fourier Transform (DFT) in order to extract multiple statistical features, as well as K-means clustering and multilayer perceptron neural network technique to identify the location of a license plate in an image. The method is compared with the state-of-the-art research in the field and the effectiveness of the method is evaluated for various types of license plates with different scripts.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | License Plate Localization, Vertical Projection, Statistical Features, MLP Neural Network |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Feb 2020 12:09 |
Last Modified: | 25 Jun 2023 22:10 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/aiar.2018.8769804 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157457 |