Revolutionizing law enforcement: The role of artificial intelligence in license plate recognition

Kewei Zhan

doi:10.54254/2755-2721/17/20230906

Research Article

Open access

Published on 23 October 2023

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Zhan,K. (2023). Revolutionizing law enforcement: The role of artificial intelligence in license plate recognition. Applied and Computational Engineering,17,32-35.

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Revolutionizing law enforcement: The role of artificial intelligence in license plate recognition

Kewei Zhan *^,1,

¹ Virginia Tech

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/17/20230906

Abstract

This paper aims to offer a comprehensive review of the current state of the art in artificial intelligence (AI) as applied to license plate recognition. With the rapidly evolving nature of AI technology, deep learning approaches have gained popularity in license plate recognition, as exemplified by the success of AlphaGo. The diversity of AI in license plate recognition is notable, with numerous studies proposing systems that have achieved high accuracy in segmentation and recognition. The process of reading license plates is complex and involves several stages, including image capture, pre-processing, license plate identification, character segmentation, and recognition. Law enforcement widely uses automatic license plate recognition (ALPR) technology for detecting and preventing criminal activities, tracking stolen vehicles, and identifying suspects. Additionally, ALPR technology can monitor travel time on significant roadways, which can provide the Department of Transportation with useful data for efficient traffic management. Overall, this paper highlights the importance of AI in license plate recognition and its potential to revolutionize the field.

Keywords

artificial intelligence, image recognition, license plate recognition

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Cite this article

Zhan,K. (2023). Revolutionizing law enforcement: The role of artificial intelligence in license plate recognition. Applied and Computational Engineering,17,32-35.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-025-7(Print) / 978-1-83558-026-4(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.17

ISSN：2755-2721(Print) / 2755-273X(Online)

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