Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system
Abstract Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) camera...
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doaj-4f9e1853020a412899147324b7e023402020-11-25T00:27:30ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812018-07-012018111710.1186/s13640-018-0298-2Optical character recognition on heterogeneous SoC for HD automatic number plate recognition systemAli Farhat0Omar Hommos1Ali Al-Zawqari2Abdulhadi Al-Qahtani3Faycal Bensaali4Abbes Amira5Xiaojun Zhai6College of Engineering, Qatar UniversityCollege of Engineering, Qatar UniversityCollege of Engineering, Qatar UniversityCollege of Engineering, Qatar UniversityCollege of Engineering, Qatar UniversityCollege of Engineering, Qatar UniversityDepartment of Electronics, Computing and Mathematics, University of DerbyAbstract Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%.http://link.springer.com/article/10.1186/s13640-018-0298-2Optical character recognitionAutomatic number plate recognition systemsFPGAHigh-level synthesisVivado |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ali Farhat Omar Hommos Ali Al-Zawqari Abdulhadi Al-Qahtani Faycal Bensaali Abbes Amira Xiaojun Zhai |
spellingShingle |
Ali Farhat Omar Hommos Ali Al-Zawqari Abdulhadi Al-Qahtani Faycal Bensaali Abbes Amira Xiaojun Zhai Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system EURASIP Journal on Image and Video Processing Optical character recognition Automatic number plate recognition systems FPGA High-level synthesis Vivado |
author_facet |
Ali Farhat Omar Hommos Ali Al-Zawqari Abdulhadi Al-Qahtani Faycal Bensaali Abbes Amira Xiaojun Zhai |
author_sort |
Ali Farhat |
title |
Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system |
title_short |
Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system |
title_full |
Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system |
title_fullStr |
Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system |
title_full_unstemmed |
Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system |
title_sort |
optical character recognition on heterogeneous soc for hd automatic number plate recognition system |
publisher |
SpringerOpen |
series |
EURASIP Journal on Image and Video Processing |
issn |
1687-5281 |
publishDate |
2018-07-01 |
description |
Abstract Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%. |
topic |
Optical character recognition Automatic number plate recognition systems FPGA High-level synthesis Vivado |
url |
http://link.springer.com/article/10.1186/s13640-018-0298-2 |
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