Deep Learning-Based Vehicle Number Plate Recognition in Smoggy Environments with Image Enhancement

Clicks: 1
ID: 312769
2025
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #453 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
The vehicles on road is increasing quickly due to the ongoing industrial revolution and the quick development of technology. Along with the rise in motor vehicles, there has also been a rise in traffic infractions, which can sometimes be a contributing factor to criminal activity. Existing surveillance systems are seriously threatened by these issues. This study's objective is improve existing surveillance by implementing reliable method for automatic license plate recognition.  In order to standardize input data and enhance model generalization, the research presents a strong method for automatic car license plate recognition and identification. Preprocessing and data augmentation are highlighted. Picture scaling to provide consistent dimensions, pixel value normalization for efficient model convergence, and other preprocessing methods like contour detection and adapted thresholding for precise plate localization and character segmentation are important steps. Based on empirical results, the model is able to obtain an 86.30% validation accuracy with batch size 128 and demonstrates that model has excellent learning capabilities and performs well when applied to new data. The recommended system, which uses deep learning architecture with advanced preprocessing techniques, provides an effective complete solution for detecting and recognizing automobile license plates. The remarkable degree of precision attained throughout prolonged testing proves its practical viability, opening the path for future advances in autonomous vehicle tracking and traffic control systems.
Reference Key
imported_1777056652_69ebbb8cd581a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Abeeha Ijaz1
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1012
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.