A design of license plate recognition system using convolutional neural network

Marzuki, P.; Syafeeza, A. R.; Wong, Yan Chiew; Hamid, Norihan Abdul; Alisa, Ali Nur; Ibrahim, Marc

doi:10.11591/ijece.v9i3.pp2196-2204

Cited by 20 publications

(12 citation statements)

References 17 publications

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“…Table 2 shows some of the proposed techniques of LPR from the literature, which utilize neural networks for character recognition. The deep neural network has also been used in the literature [5]. However, the deep architectures of neural networks are not considered in this paper.…”

Section: Neural Networkmentioning

confidence: 99%

License plate recognition for campus auto-gate system

Yaacob

Alkahtani

Noman

et al. 2021

IJEECS

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Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition.

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Section: Neural Networkmentioning

confidence: 99%

License plate recognition for campus auto-gate system

Yaacob

Alkahtani

Noman

et al. 2021

IJEECS

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“…The error, computed as the difference between yk and the desired output is integrated into the Levenberg-Marquardt algorithm for network training [23][24]. Convergence of error is monitored via MSE.…”

Section: Multilayered Perceptron Networkmentioning

confidence: 99%

Electrocardiogram profiling of myocardial infarction history using MLP and HMLP networks

Gani

Nordin

Yassin

et al. 2020

IJEECS

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Narrowing of coronary arteries caused by cholesterol deposits deprives heart tissues of oxygen. In prolonged conditions, these will result in myocardium infarction. The presence of damage tissues modifies the normal sinus rhythm and this can be detected using electrocardiogram (ECG). Hence, this paper characterized history of myocardial infarction from survivors using QRS power ratio features from the ECG. Subsequent profiling is performed using multilayered perceptron (MLP) and hybrid multilayered perceptron (HMLP) networks. ECG with history of anterior and inferior infarctions, along with healthy controls is obtained from PTB Diagnostic ECG Database. The signal is initially pre-processed and the power ratio features are extracted for low- and mid-frequency components. The features are then used as input vector to the MLP and HMLP networks. The optimized MLP has attained accuracies of 99.2% for training and 98.0% for testing. Meanwhile, the optimized HMLP managed to achieve accuracies of 99.4% for training and 97.8% for testing. Despite the similarities in network performance, MLP provides a better alternative due to the reduced computational requirements by as much as 30%.

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“…So, there is an urgency to make the license plate recognition (LPR) system more dynamic, plaint to the diversification of backgrounds and other environmental factors like illumination, angle, noise, and distortion, which cause a challenge for ALPR. Multiple advanced computer vision technologies and artificial intelligence algorithms have been proposed to identify vehicle licenses in constrained backgrounds [5], [7], [9]. However, in situations of varying backgrounds, these existing systems start to face difficulty in discerning license plates (LP).…”

Section: Introductionmentioning

confidence: 99%

Mobile Registration Number Plate Recognition Using Artificial Intelligence

Ali¹,

Usama²,

Khan

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Automatic License Plate Recognition (ALPR) for years has remained a persistent topic of research due to numerous practicable applications, especially in the Intelligent Transportation system (ITS). Many currently available solutions are still not robust in various real-world circumstances and often impose constraints like fixed backgrounds and constant distance and camera angles. This paper presents an efficient multi-language repudiate ALPR system based on machine learning. Convolutional Neural Network (CNN) is trained and fine-tuned for the recognition stage to become more dynamic, plaint to diversification of backgrounds. For license plate (LP) detection, a newly released YOLOv5 object detecting framework is used. Data augmentation techniques such as gray scale and rotatation are also used to generate an augmented dataset for the training purpose. This proposed methodology achieved a recognition rate of 92.2%, producing better results than commercially available systems, PlateRecognizer (67%) and OpenALPR (77%). Our experiments validated that the proposed methodology can meet the pressing requirement of real-time analysis in Intelligent Transportation System (ITS).

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A design of license plate recognition system using convolutional neural network

Cited by 20 publications

References 17 publications

License plate recognition for campus auto-gate system

License plate recognition for campus auto-gate system

Electrocardiogram profiling of myocardial infarction history using MLP and HMLP networks

Mobile Registration Number Plate Recognition Using Artificial Intelligence

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