Automatic Number Plate Recognition Based Image Processing

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume :12 | Issue : 01 | Page : –
By
vector

Sakshi Trivedi,

vector

Parul Panchal,

vector

Tushar Galaiya,

  1. Student, Department of Electronics, Birla Vishvakarma Mahavidyalaya Engineering College (BVM), Anand, Gujarat, India
  2. Assistant Professor, Department of Electronics, Birla Vishvakarma Mahavidyalaya Engineering College (BVM), Anand, Gujarat, India
  3. Student, Department of Electronics, Birla Vishvakarma Mahavidyalaya Engineering College (BVM), Anand, Gujarat, India

Abstract

“] document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_146615’);});Edit Abstract & Keyword

In most countries, traffic control and vehicle owner identification have become important problems. It is almost impossible to identify vehicle owners who break rules of the traffic, especially those driving at high speeds. Another problem inhibits traffic officers from catching the offenders in most cases because solving traffic offenders involves retrieving license plate numbers from fast-moving vehicles. Automatic Number Plate Recognition (ANPR) systems have been developed as an effective solution to address this issue. There are many ANPR systems, but each brings a range of obstacles to recognize, including vehicle speed, non-standardized license plates, different language on license plates, and varying lighting conditions, which can degrade recognition. Each of these systems functions within these limitations. Image size, success rate and processing time for ANPR are discussed in this paper and evaluated with different ANPR approaches. An extension to ANPR is proposed at the end of the paper.

Keywords: Character Segmentation, Image Segmentation, Number Plate, Optical Character Recognition (OCR), Artificial Neural Network (ANN), Automatic Number Plate Recognition (ANPR).

[This article belongs to Journal of Image Processing & Pattern Recognition Progress (joipprp)]

How to cite this article:
Sakshi Trivedi, Parul Panchal, Tushar Galaiya. Automatic Number Plate Recognition Based Image Processing. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(01):-.
How to cite this URL:
Sakshi Trivedi, Parul Panchal, Tushar Galaiya. Automatic Number Plate Recognition Based Image Processing. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(01):-. Available from: https://journals.stmjournals.com/joipprp/article=2025/view=0

“]document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_146615’);});Edit

References

  1. Li M, Zhang L. Deep Learning-based License Plate Recognition in IoT Smart Parking Systems using YOLOv6 Algorithm. International Journal of Advanced Computer Science & Applications. 2023 Dec 1;14(12).
  2. Tang J, Wan L, Schooling J, Zhao P, Chen J, Wei S. Automatic number plate recognition (ANPR) in smart cities: A systematic review on technological advancements and application cases. Cities. 2022 Oct 1;129:103833.
  3. Ibitoye O, Ejidokun T, Dada O, Omitola O. Convolutional neural network-based license plate recognition techniques: A short overview. In2020 International Conference on Computational Science and Computational Intelligence (CSCI) 2020 Dec 16 (pp. 1529-1532). IEEE.
  4. Arora M, Jain A, Rustagi S, Yadav T. Automatic number plate recognition system using optical character recognition. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2019 Apr;5(2):986-92.
  5. Laroca R, Severo E, Zanlorensi LA, Oliveira LS, Gonçalves GR, Schwartz WR, Menotti D. A robust real-time automatic license plate recognition based on the YOLO detector. In2018 international joint conference on neural networks (ijcnn) 2018 Jul 8 (pp. 1-10). IEEE.
  6. Kulkarni P, Khatri A, Banga P, Shah K. Automatic number plate recognition (anpr) system for indian conditions. In2009 19th international conference Radioelektronika 2009 Apr 22 (pp. 111-114). IEEE.
  7. Salau AO, Yesufu TK, Ogundare BS. Vehicle plate number localization using a modified GrabCut algorithm. Journal of King Saud University-Computer and Information Sciences. 2021 May 1;33(4):399-407.
  8. Silva SM, Jung CR. License plate detection and recognition in unconstrained scenarios. InProceedings of the European conference on computer vision (ECCV) 2018 (pp. 580-596).
  9. Gloger O, Kühn J, Stanski A, Völzke H, Puls R. A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images. Magnetic resonance imaging. 2010 Jul 1;28(6):882-97.
  10. Merad D, Aziz KE, Thome N. Fast people counting using head detection from skeleton graph. In2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance 2010 Aug 29 (pp. 233-240). IEEE.
Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 21/11/2024
Accepted 18/12/2024
Published 08/01/2025
124