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Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

Published: 01 February 2013 Publication History
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  • Abstract

    Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

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    cover image IEEE Transactions on Circuits and Systems for Video Technology
    IEEE Transactions on Circuits and Systems for Video Technology  Volume 23, Issue 2
    February 2013
    181 pages

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    IEEE Press

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    Published: 01 February 2013

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    • (2024)CarAI: Car Inspection with Artificial IntelligenceProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657589(1241-1245)Online publication date: 30-May-2024
    • (2024)Handwritten Bangla character recognition using convolutional neural networks: a comparative study and new lightweight modelNeural Computing and Applications10.1007/s00521-023-09008-836:1(337-348)Online publication date: 1-Jan-2024
    • (2023)A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machinesCollective Intelligence10.1177/263391372311708252:2Online publication date: 1-Apr-2023
    • (2023)Benchmarking Probabilistic Deep Learning Methods for License Plate RecognitionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.327853324:9(9203-9216)Online publication date: 1-Sep-2023
    • (2023)An Efficient and Unified Recognition Method for Multiple License Plates in Unconstrained ScenariosIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.323774324:5(5376-5389)Online publication date: 1-May-2023
    • (2023)Boosting One-Stage License Plate Detector via Self-Constrained Contrastive AggregationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.324128333:8(4204-4216)Online publication date: 1-Aug-2023
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