Automated new license plate recognition in Egypt

Massoud, Mohammed A. M.; Sabee, M.; Gergais, M.; Bakhit, R.

doi:10.1016/j.aej.2013.02.005

Cited by 53 publications

(30 citation statements)

References 13 publications

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“…The previous tables show that the detection accuracy is improved using the proposed model compared to the conventional LPR (Massoud, 2013). This improvement is gained by using SWT which emphases letter strokes and ignores noise strokes and by employing Fuzzy ARTMAP instead of template matching to achieve better character recognition accuracy.…”

Section: Resultsmentioning

confidence: 86%

“…The proposed model (Sobel Edge detection with SWT image map) performance is compared with a conventional LPR System for Egyptian license plate that was presented by (Massoud, 2013) where Sobel Edge detection with binary image map and using template matching technique was used. The proposed model and the conventional LPR (Massoud, 2013) are tested using two character recognition techniques, template matching and the Fuzzy ARTMAP classifier. The classifiers are trained using 722 Arabic letters images which are resized to 70×50.…”

Section: Resultsmentioning

confidence: 99%

“…The license plate has a fixed size 17×32 cm divided horizontally into two parts. The ratio between the upper colored side and the white part is 1:2 (Massoud, 2013).…”

Section: Jcsmentioning

confidence: 99%

See 2 more Smart Citations

Egyptian License Plate Recognition Using Enhanced Stroke Width Transformation and Fuzzy Artmap

Youssef¹,

El-Mahallawy²,

Badr³

2014

Journal of Computer Science

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License Plate Recognition (LPR) is the most important type of Intelligent Transportation System (ITS). LPR is used in many different types of ITS like electronic payment systems, toll station, parking fees, freeway and arterial management systems for traffic surveillance. Few years ago, Egyptians government changed the car license plate to include letters and numbers. So the needs for efficient LPR System for the new license plate are increased in different ITS fields. This study presents an enhanced LPR detection algorithm for the new Egyptian licenses plate. The detection enhancement is done using Stroke Width Transform algorithm to extract letters from candidate areas combined with Fuzzy ARTMAP classifier. Stroke Width Transform (SWT) is a state of art algorithm developed by Microsoft Research Lab for detecting text in natural scene, it seeks to find the value of stroke width for each image pixel and demonstrate its use on the task of text detection in natural images. This study is focusing on detecting Arabic letters in the candidate license plate area using SWT image map instead of binary image map where not all Arabic letters have uniformly stroke width and some letters have a dot above and below it. The proposed model shows 26% detection accuracy enhancement than conventional LPR systems (Sobel Edge detection with binary image map using template matching technique).

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Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Egyptian License Plate Recognition Using Enhanced Stroke Width Transformation and Fuzzy Artmap

Youssef¹,

El-Mahallawy²,

Badr³

2014

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…In Egypt: In [14], authors present a new and simple technique for building Egyptian vehicle's License Plate Recognition (LPR) system. In their work, the authors propose similar approach for LPR system, which is locating the plate region, character recognition.…”

Section: Related Workmentioning

confidence: 99%

“…First, using a series of image processing techniques to detect, normalize and enhance the image of the sea, and then optical character recognition (OCR) to extract the letters and number plates [6,7,22,20,15,11,3]. Automatic recognition is a topic that attracts many researchers in all over the world such as in Argentina, Australia, China Germany, USA and Europe [4,8,14,9,21].…”

Section: Related Workmentioning

confidence: 99%

Supervised Learning Method for Mobile International Number Plate Recognition

Tran

Nguyen

2014

Proceedings of the 16th International Conference on Information Integration and Web-Based Applications &Amp; Services

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The automatic number-plate recognition (ANPR) has become more important nowadays, especially for security management, parking management and law enforcement. There are many ANPR systems introduced but almost of them worked under restricted conditions, such as fixed location of ANPR cameras, processing speed and hardware resources. In this paper, we build the Mobile International Number Plate Recognition (MINPR) system which is exploited and implemented on the Android mobile phone platform to solve these problems. The MINPR includes three main steps: using AdaBoots classification with Haar-like features to locate the area which contains the number plate, applying morphology method to separate characters within the number plate, and using neural network for characters recognition.

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Blockchain for tracking serial numbers in money exchanges

Mohamed

Aziz

Mohamed

et al. 2019

Intell Sys Acc Fin Mgmt

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Money exchange is one of the most common day-to-day activities performed by humans in the daily market. This paper presents an approach to money tracking through a blockchain. The proposed approach consists of three main components: serial number localization, serial number recognition, and a blockchain to store all transactions and ownership transfers. The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. We conducted a user study in real-time with 21 users, and the mean accuracy across all users was 86.42%. Each user gave us feedback on the proposed approach, and most of them welcomed the idea. K E Y W O R D Simage processing, banknote serial number recognition (SNR), blockchain

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Automated new license plate recognition in Egypt

Cited by 53 publications

References 13 publications

Egyptian License Plate Recognition Using Enhanced Stroke Width Transformation and Fuzzy Artmap

Egyptian License Plate Recognition Using Enhanced Stroke Width Transformation and Fuzzy Artmap

Supervised Learning Method for Mobile International Number Plate Recognition

Blockchain for tracking serial numbers in money exchanges

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