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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
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Sensors and Materials, Volume 30, Number 10(1) (2018)
Copyright(C) MYU K.K.
pp. 2247-2265
S&M1673 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2018.1852
Published: October 12, 2018

License Plate Identification from Myanmar Vehicle Images under Different Environmental Conditions [PDF]

Ohnmar Khin, Montri Phothisonothai, and Somsak Choomchuay

(Received December 18, 2017; Accepted August 28, 2018)

Keywords: car license plate recognition (CLPR), bounding box, horizontal and vertical dilation, skew angle detection, license plate detection, deep learning, neural networks

We have developed a license plate identification method for Myanmar vehicles that are captured under dissimilar conditions, e.g., angle of image capturing, different types of license plates, and real environmental conditions. In this study, car license plate recognition (CLPR), bounding box, horizontal and vertical dilations, skew angle detection, and plate detection were proposed to identify license numbers from different vehicle images. To recognize the characters, a new algorithm based on deep learning, a subset of artificial intelligence (AI), is proposed. The neural nets are progressing rapidly in many fields. The applied model of neural network is used for classification. The recognition part is a very challenging task. Compared with the traditional method, the neural network has obvious advantages. The benefit of this research is to eliminate the need of license plate recognition (LPR) under different conditions. In mobile phones, there are many sensors used to detect the presence of nearby objects. Accelerometers in mobile phones are used. Developed for the Samsung mobile phone, sensors can yield sensor readings but it not much else. Each car was viewed from four different angles under different conditions. In our experiment, the results showed an average accuracy of 97%, which was substantially applied to license plate identification under different environmental conditions. To extend the experiment, the vehicle images were also collected under different conditions, such as dark and cloudy weather and various sizes and positions of plates.

Corresponding author: Ohnmar Khin


Cite this article
Ohnmar Khin, Montri Phothisonothai, and Somsak Choomchuay, License Plate Identification from Myanmar Vehicle Images under Different Environmental Conditions, Sens. Mater., Vol. 30, No. 10, 2018, p. 2247-2265.



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