OCR-Based Hardware Implementation for Qatari Number Plate on the Zynq SoC

Farhat, Ali; Al-Zawqari, Ali; Hommos, Omar; Al-Qahtani, Abdulhadi; Bensaali, Fayçal; Amira, Abbes; Zhai, Xiaojun

doi:10.1109/ieeegcc.2017.8448145

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…In [ 24 ], 99.5% accuracy rate is achieved by using HD cameras for over 2790 characters tested in real time. CCA with OCR algorithms were used for Qatar number plates format.…”

Section: Discussionmentioning

confidence: 99%

“…The recognition rate of 98% is achieved. In [ 24 ], the recognition rate of similar system is 99.5%. However, systems with HD cameras are computationally expensive, involve higher cost, greater memory requirements and also have processing time constraints.…”

Section: Discussionmentioning

confidence: 99%

“…Edge detection algorithms are commonly used to extract number plates by finding all the rectangles in the acquired images [ 24 , 25 , 26 ]. Mostly, the car body and number plate area have a clear color transition.…”

Section: Number Plate Extraction Methodsmentioning

confidence: 99%

“…Pixel connectivity is used for segmentation in [ 23 , 24 , 26 , 27 , 45 , 47 , 103 ]. In [ 26 ], 958 High Definition (HD) images with various conditions are tested using connected component labeling with 99.75% segmentation accuracy.…”

Section: Number Plate Segmentation Methodsmentioning

confidence: 99%

“…In [ 23 ], a limited dataset of 50 images were tested using connected component labeling and morphological method, achieving a segmentation rate of 91%. In [ 24 ], using connected components analysis, an accuracy rate of 99.5% was recorded by employing HD images in various light and weather conditions. In [ 45 ], the extracted plate area is binarized and labelled to get the numbers.…”

Section: Number Plate Segmentation Methodsmentioning

confidence: 99%

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Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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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.

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“…In [ 24 ], 99.5% accuracy rate is achieved by using HD cameras for over 2790 characters tested in real time. CCA with OCR algorithms were used for Qatar number plates format.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Number Plate Extraction Methodsmentioning

confidence: 99%

Section: Number Plate Segmentation Methodsmentioning

confidence: 99%

Section: Number Plate Segmentation Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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An Imperious Verdict for The Recognition of Vehicles Number-Plate Using an Innovative Methodology

Sunil

Samuel

Prasannakumar

2022

2022 Second International Conference on Interdisciplinary Cyber Physical Systems (ICPS)

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Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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OCR-Based Hardware Implementation for Qatari Number Plate on the Zynq SoC

Cited by 3 publications

References 5 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

An Imperious Verdict for The Recognition of Vehicles Number-Plate Using an Innovative Methodology

Computer Vision Applications in Intelligent Transportation Systems: A Survey

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