HD number plate localization and character segmentation on the Zynq heterogeneous SoC.

Journal article


Al-Zawqari, Ali, Hommos, Omar, Al-Qahtani, Abdulhadi, Farhat, Ali, Bensaali, Faycal, Zhai, Xiaojun and Amira, Abbes 2018. HD number plate localization and character segmentation on the Zynq heterogeneous SoC. Journal of Real-Time Image Processing. https://doi.org/10.1007/s11554-017-0747-7
AuthorsAl-Zawqari, Ali, Hommos, Omar, Al-Qahtani, Abdulhadi, Farhat, Ali, Bensaali, Faycal, Zhai, Xiaojun and Amira, Abbes
Abstract

Automatic number plate recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. A typical ANPR system consists of three main stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). In recent years, to provide a better recognition rate, high-definition (HD) cameras have started to be used. However, most known techniques for standard definition (SD) are not suitable for real-time HD image processing due to the computationally intensive cost of processing several-folds more of image pixels, particularly in the NPL stage. In this paper, algorithms suitable for hardware implementation for NPL and CS stages of an HD ANPR system are presented. Software implementation of the algorithms was carried on as a proof of concept, followed by hardware implementation on a heterogeneous system-on-chip (SoC) device that contains an ARM processor and a field-programmable gate array (FPGA). Heterogeneous implementation of these stages has shown that this HD NPL algorithm can localize a number plate in 16.17 ms, with a success rate of 98.0%. The CS algorithm can then segment the detected plate in 0.59 ms, with a success rate of 99.05%. Both stages utilize only 21% of the available on-chip configurable logic blocks.

KeywordsNumber plate recognition; Character segmentation; Heterogenous
Year2018
JournalJournal of Real-Time Image Processing
PublisherSpringer
ISSN18618200
18618219
Digital Object Identifier (DOI)https://doi.org/10.1007/s11554-017-0747-7
Web address (URL)http://hdl.handle.net/10545/622890
hdl:10545/622890
Publication dates06 Feb 2018
Publication process dates
Deposited13 Aug 2018, 08:01
Accepted30 Dec 2017
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Archived with thanks to Journal of Real-Time Image Processing

ContributorsQatar University and University of Derby
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