Journal article
Automatic number plate recognition: A detailed survey of relevant algorithms
Sensors, Vol.21(9), Article 3028
2021
Abstract
Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.
Details
- Title
- Automatic number plate recognition: A detailed survey of relevant algorithms
- Authors/Creators
- . Lubna (Author/Creator)N. Mufti (Author/Creator) - University of Engineering and Technology LahoreS.A.A. Shah (Author/Creator) - Murdoch University
- Publication Details
- Sensors, Vol.21(9), Article 3028
- Publisher
- MDPI AG
- Identifiers
- 991005540217307891
- Copyright
- © 2021 The Authors
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Journal article
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- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.942 Handwritten Text Recognition
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