An Overview of Image Analysis Algorithms for License Plate Recognition

Aboura Khalid () and Al-Hmouz Rami ()
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Aboura Khalid: University of Dammam, College of Business Administration, Dammam, Saudi Arabia
Al-Hmouz Rami: King Abdulaziz University, Department of Electrical and Computer Engineering, Jeddah, Saudi Arabia

Organizacija, 2017, vol. 50, issue 3, 285-295

Abstract: Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate.Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates.Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%.Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.

Keywords: Image Analysis; Probabilistic Modeling; Signal Processing; License Plate Recognition (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:organi:v:50:y:2017:i:3:p:285-295:n:2

DOI: 10.1515/orga-2017-0014

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