Automatic license plate detection in hazardous condition

Azam, Samiul; Islam, Monirul

doi:10.1016/j.jvcir.2016.01.015

Cited by 80 publications

(46 citation statements)

References 23 publications

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“…The CCA and kohonen neural network [9] is used to segment characters from Indian LPs. The CCA and Contour detection is used for character segmentation in Bangladesh and Iraq license plates [10][11][12]. CIE-LAB color space, OTSU segmentation and CCA are used to extract characters from a Pakistani vehicle license plate.…”

Section: Related Workmentioning

confidence: 99%

“…So, for multinational vehicle LPs, we must have prior knowledge of foreground polarities in order to separate foreground from background as multinational VLPs have different backgrounds and foregrounds polarities. After getting binary image, we change its background polarity to bright if needed by using (11). The next step is to extract the ROI which contains only required characters by discarding redundant area of license plate.…”

Section: Color Countmentioning

confidence: 99%

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An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

Asif

Khan

2020

Applied Sciences

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License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions.

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Section: Related Workmentioning

confidence: 99%

Section: Color Countmentioning

confidence: 99%

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

Asif

Khan

2020

Applied Sciences

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“…CCA (Connected Component Analysis) integrated technique has been implemented as one of the significant methodologies for the processing of the binary images [2,13]. In [14] an algorithm has been implemented for tracking out the connected objects through utilizing the contour detection. The objects that get selected to be the desired candidate within these connected objects possess the identical geometrical attributes as that of the vehicular plate.…”

Section: Related Workmentioning

confidence: 99%

“…Another procedure integrated with Radon transform has been implemented in [5] for correcting the rotational problem in accordance with horizontal perspective. In [14] the binary perimeter image has been utilized firstly by applying Bernsen binarization and then Radon transform has been utilized for tilt angle correction. The color combination between the characters and the vehicular number plates is a unique feature which is also utilized for tracking the desired plate region.…”

Section: Related Workmentioning

confidence: 99%

A Vehicular License Plate Recognition Framework For Skewed Images

2018

TIIS

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Vehicular license plate (LP) recognition system has risen as a significant field of research recently because various explorations are currently being conducted by the researchers to cope with the challenges of LPs which include different illumination and angular situations. This research focused on restricted conditions such as using image of only one vehicle, stationary background, no angular adjustment of the skewed images. A real time vehicular LP recognition scheme is proposed for the skewed images for detection, segmentation and recognition of LP. In this research, a polar co-ordinate transformation procedure is implemented to adjust the skewed vehicular images. Besides that, window scanning procedure is utilized for the candidate localization that is based on the texture characteristics of the image. Then, connected component analysis (CCA) is implemented to the binary image for character segmentation where the pixels get connected in an eight-point neighbourhood process. Finally, optical character recognition is implemented for the recognition of the characters. For measuring the performance of this experiment, 300 skewed images of different illumination conditions with various tilt angles have been tested. The results show that proposed method able to achieve accuracy of 96.3% in localizing, 95.4% in segmenting and 94.2% in recognizing the LPs with an average localization time of 0.52s.

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“…Finally, true candidate is extracted using textual and geometrical properties and localized 97.4% on the dataset of 501 images. S. Azam and M. Islam [35], proposed a new LPL technique that used mask in frequency domain to filter out the rain streaks from rainy image and Radon transform for the tilt correction. This method can handle low contrast, blurry, rainy, night, and foggy images with 94% detection rate.…”

Section: IImentioning

confidence: 99%

An overview of Various Techniques for Automatic License Plate Localization

2018

IJRASET

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Automatic license plate detection in hazardous condition

Cited by 80 publications

References 23 publications

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

A Vehicular License Plate Recognition Framework For Skewed Images

An overview of Various Techniques for Automatic License Plate Localization

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