Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems
Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of co...
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doaj-771d3e76116f45b6ab17e40f50a30e672021-03-30T01:34:05ZengIEEEIEEE Access2169-35362020-01-018929079291710.1109/ACCESS.2020.29930089088975Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation SystemsIrina Valeryevna Pustokhina0Denis Alexandrovich Pustokhin1Joel J. P. C. Rodrigues2https://orcid.org/0000-0001-8657-3800Deepak Gupta3https://orcid.org/0000-0002-3019-7161Ashish Khanna4K. Shankar5https://orcid.org/0000-0002-2803-3846Changho Seo6https://orcid.org/0000-0002-0779-3539Gyanendra Prasad Joshi7https://orcid.org/0000-0002-5446-288XDepartment of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, RussiaDepartment of Logistics, State University of Management, Moscow, RussiaFederal University of Piauí (UFPI), Teresina, BrazilDepartment of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, IndiaDepartment of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, IndiaDepartment of Computer Applications, Alagappa University, Karaikudi, IndiaDepartment of Convergence Science, Kongju National University, Gongju, South KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul, South KoreaDue to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.https://ieeexplore.ieee.org/document/9088975/Intelligent transportation systemconvolutional neural networkK-meanstraffic managementvehicle license plate recognitioncharacter recognition |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Irina Valeryevna Pustokhina Denis Alexandrovich Pustokhin Joel J. P. C. Rodrigues Deepak Gupta Ashish Khanna K. Shankar Changho Seo Gyanendra Prasad Joshi |
spellingShingle |
Irina Valeryevna Pustokhina Denis Alexandrovich Pustokhin Joel J. P. C. Rodrigues Deepak Gupta Ashish Khanna K. Shankar Changho Seo Gyanendra Prasad Joshi Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems IEEE Access Intelligent transportation system convolutional neural network K-means traffic management vehicle license plate recognition character recognition |
author_facet |
Irina Valeryevna Pustokhina Denis Alexandrovich Pustokhin Joel J. P. C. Rodrigues Deepak Gupta Ashish Khanna K. Shankar Changho Seo Gyanendra Prasad Joshi |
author_sort |
Irina Valeryevna Pustokhina |
title |
Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems |
title_short |
Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems |
title_full |
Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems |
title_fullStr |
Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems |
title_full_unstemmed |
Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems |
title_sort |
automatic vehicle license plate recognition using optimal k-means with convolutional neural network for intelligent transportation systems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way. |
topic |
Intelligent transportation system convolutional neural network K-means traffic management vehicle license plate recognition character recognition |
url |
https://ieeexplore.ieee.org/document/9088975/ |
work_keys_str_mv |
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