Vehicle license plate detection and recognition using symbol analysis

Číka, Petr; Zukal, Martin; Šebela, Miroslav

doi:10.1109/tsp.2011.6043661

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Likewise, this paper talked about the tilt remedy of vehicle LP. In the [18], proposed a framework for identifying and perceiving the characters and quantities of Czech Republic LPs. Their framework comprises of number of modules that rely on upon each other.…”

Section: Related Workmentioning

confidence: 99%

Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm

Alaidi¹,

Alsaidi²,

Yahya

2017

eduj

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This paper presents an automated car license plate recognition system applied for Iraqi vehicle plate number that developed and applied to be used in control and law enforcement related applications. In this work, the proposed license plate recognition consists of three basic stages (preprocessing, license plate localization, license plate recognition). The license plate images are pre-processed through convert image to grayscale and apply morphological transformation filter not convert the result to binary image. Then, blurs the binary image using Gaussian filter and find all contour in image using OpenCV library. In the license plate localization KNN (k-Nearest Neighbors) algorithm are used to find all possible characters in the image. The last step is done by Crop the part of image with highest candidate license plate and apply the preprocessing and license plate localization again to find and recognize all part of license plate in the cropped image.

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Section: Related Workmentioning

confidence: 99%

Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm

Alaidi¹,

Alsaidi²,

Yahya

2017

eduj

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show abstract

“…One of the challenges in implementing vehicle recognition and classification systems is to design a model that works across a wide variety of users and environments. Existing vehicle recognition system mainly uses license plate information as a feature for recognition (Deb et al, 2009;Conci et al, 2009;Ozbay and Ercelebi, 2005;Cika et al, 2011). If it is tampered or replaced, effective system along with this feature has to be used for identifying whether the car is authenticated or not.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Approach for Detection and Recognition of Vehicles

Selvanayaki¹,

Somasundaram²

2015

Journal of Computer Science

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On road vehicles have increased in numbers and monitoring them is a challenging task. In common areas of public crowd people and vehicles are common objects for monitoring. The proposed system aims at detecting number plate information indicating the possibility for security relevant issues. Existing system performs recognition mainly by using license plate alone. Addition of the features (logo, colour, shape) will increase the security of the system. Identification of the number plate region has been done by Blob detection method at the predefined aspect ratio. After detection, extraction of the number plate information using Eigen value regularization method. Further, two methodology included in this study are, identifying the tampered region in a car image either by extracting HoG feature in the spatial domain or block differences in DCT coefficients and their corresponding histogram in the transform domain respectively. Experimental results for the given car dataset describes the identification of the number plate region and tampered region quantitatively. The work presents detailed results of how the proposed approach gives better results using HoG approach. The approach gives good results in videos of cars recorded in frontal view in good lighting conditions. The paper in overall suggest a hybrid approach for detecting number plate information in cars taken in good lighting conditions.

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“…In this paper, the authors Cika, and et al [6], proposed a system for detecting and recognizing the characters and numbers of Czech Republic LPs. The system consists of a number of units that are dependent on one another.…”

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confidence: 99%

Review on License plate recognition algorithms in images and video

Mohammed¹,

Jasem

2020

IJERAT

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It is possible to identify the existing license plate (LPR) pictures or videos depending on the algorithms used and in general it is possible that the algorithms consist of three steps in terms of processing are 1) extracting the part of the license plate; 2) manufacture of the license plate section. and 3) recognition of each character. The process is difficult due to the diversity of the panels' formats, as well as the lack of sufficient and uniform lighting during image acquisition, so it will be constrained under conditions such as fixed lighting, speed, specific roads, and other conditions. Several LPR technologies were developed in images and videos. This paper is a review of the authors work in this field and extracting the best results that the researchers reached to evaluate the LPR algorithm.

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Vehicle license plate detection and recognition using symbol analysis

Cited by 5 publications

References 8 publications

Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm

Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm

Hybrid Approach for Detection and Recognition of Vehicles

Review on License plate recognition algorithms in images and video

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