A Cascaded Vision Transformer for Precise Identification of Vehicle Number Plate

S NirmalKumar, P Kalyanasundaram, P S Prakash Kumar, G Gowtham, N Praveen, N Yashwanth

2025

Abstract

Aim: The present investigation centers on the examination of License Plate Detection (LPD) methodologies employing Vision Transformer (ViT) technology to establish a sophisticated, efficient, dependable, and scalable framework for the real-time detection and recognition of vehicle license plates. The principal aim of this scholarly pursuit is to harness the capabilities of ViT to augment predictive precision in contrast to conventional Deep Convolutional Neural Networks (DCNN), which have been extensively utilized for analogous undertakings. The efficacy of the system is assessed by juxtaposing the performance of a ViT-based model with that of an independent DCNN model under uniform testing circumstances. The experimental analysis is segmented into two cohorts: Group 1, which encompasses ten distinct DCNN-based models evaluated for license plate detection, each exhibiting varying degrees of accuracy, and Group 2, which integrates an advanced ViT-based model specifically engineered for precise detection and recognition of vehicle license plates. The findings obtained elucidate that DCNN models achieve an accuracy range spanning from 84% to 90%, whereas the ViT model exhibits enhanced effectiveness with an accuracy range of 91% to 96%. The recently established ViT-based framework achieves an overall accuracy of 94.5%, surpassing the 90.00% accuracy of the individual DCNN model. The evaluation metrics include a maximum disparity of 10.50, a minimum of 2.00, a step increment of 0.10, and a significance level of p < 0.05. These findings substantiate the viability of ViT in LPD applications, confirming its potential for deployment in intelligent transportation, vehicle monitoring, traffic regulation, and security surveillance.

Paper Citation

in Harvard Style

NirmalKumar S., Kalyanasundaram P., Prakash Kumar P., Gowtham G., Praveen N. and Yashwanth N. (2025). A Cascaded Vision Transformer for Precise Identification of Vehicle Number Plate. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 587-594. DOI: 10.5220/0013902400004919

in Bibtex Style

@conference{icrdicct`2525,
author={S NirmalKumar and P Kalyanasundaram and P S Prakash Kumar and G Gowtham and N Praveen and N Yashwanth},
title={A Cascaded Vision Transformer for Precise Identification of Vehicle Number Plate},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={587-594},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013902400004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - A Cascaded Vision Transformer for Precise Identification of Vehicle Number Plate
SN - 978-989-758-777-1
AU - NirmalKumar S.
AU - Kalyanasundaram P.
AU - Prakash Kumar P.
AU - Gowtham G.
AU - Praveen N.
AU - Yashwanth N.
PY - 2025
SP - 587
EP - 594
DO - 10.5220/0013902400004919
PB - SciTePress