PROPOSED AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM USING MACHINE LEARNING

Automatic Number Plate Recognition (ANPR) has emerged as a vital component in intelligent transportation systems,offering real-time vehicle identification for applications such as traffic enforcement, toll collection, and public safety.This paper presents a modular, cost-effective ANPR system designed for Indian vehicle license plates using Python,OpenCV, and Tesseract OCR. The system performs robust image preprocessing, contour-based plate localization, andcharacter recognition using OCR, handling varied lighting, angles, and multilingual formats common in Indianconditions. A Tkinter-based GUI enables intuitive user interaction for both image uploads and webcam capture.Experimental evaluation demonstrates over 90% accuracy in clear conditions, with efficient processing suitable forreal-time deployment. The proposed solution is scalable, hardware-light, and adaptable, making it an ideal tool fortraffic monitoring and law enforcement in resource-constrained environments

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-30
DOI
https://doi.org/10.5281/zenodo.16614634
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.00
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article

PROPOSED AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM USING MACHINE LEARNING

Zenodo (CERN European Organization for Nuclear Research)
Vehicle License Plate Recognition
article

PROPOSED AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM USING MACHINE LEARNING

article en

Abstract

Automatic Number Plate Recognition (ANPR) has emerged as a vital component in intelligent transportation systems,offering real-time vehicle identification for applications such as traffic enforcement, toll collection, and public safety.This paper presents a modular, cost-effective ANPR system designed for Indian vehicle license plates using Python,OpenCV, and Tesseract OCR. The system performs robust image preprocessing, contour-based plate localization, andcharacter recognition using OCR, handling varied lighting, angles, and multilingual formats common in Indianconditions. A Tkinter-based GUI enables intuitive user interaction for both image uploads and webcam capture.Experimental evaluation demonstrates over 90% accuracy in clear conditions, with efficient processing suitable forreal-time deployment. The proposed solution is scalable, hardware-light, and adaptable, making it an ideal tool fortraffic monitoring and law enforcement in resource-constrained environments

Zenodo (CERN European Organization for Nuclear Research)
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Openalex Percentile: Top 42%
Vehicle License Plate Recognition
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