A Multi-tiered Automatic License Plate Recognition Strategy Using YOLOv2 Detector

Automatic Car Number Plate Recognition System for Authorization

Circulation in Computer Science ◽

10.22632/ccs-2017-mcsp036 ◽

2017 ◽

Vol MCSP2017 (01) ◽

pp. 30-34

Author(s):

Somalin Sandha ◽

Debaraj Rana

Keyword(s):

Neural Network ◽

Recognition System ◽

Performance Level ◽

License Plate ◽

License Plate Recognition ◽

Human Power ◽

Automatic License Plate Recognition

In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good.

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Smart Cities Mobility Monitoring through Automatic License Plate Recognition and Vehicle Discrimination

10.1109/bmsb53066.2021.9547163 ◽

2021 ◽

Author(s):

M. Spanu ◽

M. Bertolusso ◽

G. Bingol ◽

L. Serreli ◽

C.G. Castangia ◽

...

Keyword(s):

Smart Cities ◽

License Plate ◽

License Plate Recognition ◽

Automatic License Plate Recognition

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Automatic License Plate Recognition: A Review with Indonesian Case Study

Scientific Journal of Informatics ◽

10.15294/sji.v5i2.15804 ◽

2018 ◽

Vol 5 (2) ◽

pp. 258-270

Author(s):

Aris Budianto

Keyword(s):

State Of The Art ◽

Developed Countries ◽

Transportation Systems ◽

Future Research ◽

License Plate ◽

License Plate Recognition ◽

Previous Knowledge ◽

Video Recognition ◽

Automatic License Plate Recognition

The Automatic License Plate Recognition (ALPR) has been becoming a new trend in transportation systems automation. The extraction of vehicle’s license plate can be done without human intervention. Despite such technology has been widely adopted in developed countries, developing countries remain a far-cry from implementing the sophisticated image and video recognition for some reasons. This paper discusses the challenges and possibilities of implementing Automatic License Plate Recognition within Indonesia’s circumstances. Previous knowledge suggested in the literature, and state of the art of the automatic recognition technology is amassed for consideration in future research and practice.

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Automatic license-plate recognition

Vestnik of Don State Technical University ◽

10.23947/1992-5980-2020-20-1-93-99 ◽

2020 ◽

Vol 20 (1) ◽

pp. 93-99

Author(s):

A. V. Poltavskii ◽

T. G. Yurushkina ◽

M. V. Yurushkin

Keyword(s):

License Plate ◽

License Plate Recognition ◽

Automatic License Plate Recognition

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Bio-inspired neural network with application to license plate recognition: hysteretic ELM approach

Assembly Automation ◽

10.1108/aa-11-2015-105 ◽

2016 ◽

Vol 36 (2) ◽

pp. 172-178 ◽

Cited By ~ 10

Author(s):

Liang Chen ◽

Leitao Cui ◽

Rong Huang ◽

Zhengyun Ren

Keyword(s):

Neural Network ◽

Activation Function ◽

Neural System ◽

Learning System ◽

License Plate ◽

Sigmoid Function ◽

License Plate Recognition ◽

Content Type ◽

Hidden Layer ◽

Automatic License Plate Recognition

Purpose This paper aims to present a bio-inspired neural network for improvement of information processing capability of the existing artificial neural networks. Design/methodology/approach In the network, the authors introduce a property often found in biological neural system – hysteresis – as the neuron activation function and a bionic algorithm – extreme learning machine (ELM) – as the learning scheme. The authors give the gradient descent procedure to optimize parameters of the hysteretic function and develop an algorithm to online select ELM parameters, including number of the hidden-layer nodes and hidden-layer parameters. The algorithm combines the idea of the cross validation and random assignment in original ELM. Finally, the authors demonstrate the advantages of the hysteretic ELM neural network by applying it to automatic license plate recognition. Findings Experiments on automatic license plate recognition show that the bio-inspired learning system has better classification accuracy and generalization capability with consideration to efficiency. Originality/value Comparing with the conventional sigmoid function, hysteresis as the activation function enables has two advantages: the neuron’s output not only depends on its input but also on derivative information, which provides the neuron with memory; the hysteretic function can switch between the two segments, thus avoiding the neuron falling into local minima and having a quicker learning rate. The improved ELM algorithm in some extent makes up for declining performance because of original ELM’s complete randomness with the cost of a litter slower than before.

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