Automatic Number Plate Recognition

Swanand Joshi, Pramod Jejure, Chatrasal Jadhav and Vishal Jankar

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 5, 439-448

Abstract: Automatic license plate recognition (ANPR) systems have become suitable for various applications, including traffic monitoring, law enforcement, and toll collection. This paper completes the study on automatic license plate recognition (ANPR) systems that use advanced imaging technology and machine learning algorithms to achieve accuracy in license plate verification and validation. The preparation process is adopted in various ways: image acquisition, preprocessing, location plate, character segmentation, and optical character recognition (OCR). The system, which integrates deep learning models for extraction and classification, performs better in different environments. Experimental results show that the identification accuracy of the proposed ANPR exceeds 95%, demonstrating its potential in practical applications. In addition, this paper also discusses the problems encountered in ANPR implementation, including changes in plate design, illumination, and shading, and provides suggestions for future research to improve stability and efficiency. Keywords: automatic plate recognition, image processing, machine learning, optical behavior recognition, deep learning, vehicle tracking, driving license inspection.

Keywords: Automatic License Plate Recognition; Image Acquisition; License Plate Localization; Character Segmentation; Optical Character Recognition; Identification Accuracy (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST2411476 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST2411476/IJSRST2411476 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i5:id:373

DOI: 10.32628/IJSRST2411476

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().