Improved license plate localization

Makaoui, Khalid; Guennoun, Zouhair; Ghogho, Mounir

doi:10.1109/eitech.2016.7519628

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…The algorithm was reliable and adjustable for the different lighting conditions. K. Makaoui considered the license plate localization by wavelet transform, morphological operations and projection operations method [6]. In [7], I. Bulugu proposed an algorithm for license plate localization and recognition for Tanzania car plate numbers to design algorithm for localization and recognition.…”

Section: Literature Reviewsmentioning

confidence: 99%

License Plate Detection of Myanmar Vehicle Images Captured from the Dissimilar Environmental Conditions

Khin

Phothisonothai

Choomchuay

2017

2017 International Conference on Advanced Computing and Applications (ACOMP)

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Due to the difficulty in defecting the images, in this paper, we proposed the method that can detect region of interest of the license plate for Myanmar vehicles captured from the dissimilar environmental conditions, e.g., different type of license plates, angle of image capturing, and real environmental conditions. In this paper, the horizontal and vertical dilation, skew angle detection and automatic bounding box were proposed to detect the license number from input images. In our experiment, the obtained results showed that an average detection accuracy of 99% which was substantially applied for the license plate detection from the dissimilar environmental conditions.

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Section: Literature Reviewsmentioning

confidence: 99%

License Plate Detection of Myanmar Vehicle Images Captured from the Dissimilar Environmental Conditions

Khin

Phothisonothai

Choomchuay

2017

2017 International Conference on Advanced Computing and Applications (ACOMP)

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“…Makaoui proposed LPL with morphological operations, wavelet transform, and projection operations. (6) LPR: LPR acquires impressive performance with the fast development of deep learning. In Ref.…”

Section: Introductionmentioning

confidence: 99%

License Plate Identification from Myanmar Vehicle Images under Different Environmental Conditions

Khin¹,

Phothisonothai²,

Choomchuay³

2018

Sensors and Materials

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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.

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