License plate detection using point of interest detectors and descriptors

Horák, Karel; Klečka, Jan; Nováček, Petr

doi:10.1109/tsp.2016.7760926

Cited by 3 publications

(2 citation statements)

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“…It is the vital process in the License Plate recognition (LPR) execution. This algorithm has been tested and compared each other of stability and repeatability [6]. R. Yang, H. Yin and X. Chen presented the license plate detection build by sparse auto-encoder.…”

Section: Related Workmentioning

confidence: 99%

License Plate Detection of Myanmar Vehicle Images from Dissimilar Angle Conditions

Khin

Phothisonothai

Choomchuay

2017

2017 21st International Computer Science and Engineering Conference (ICSEC)

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It has been studied that there is no established LPR(License Plate Recognition) to detect and identify the license plates from dissimilar angles. The aim of the paper is to detect the dissimilar angles of the license plate with the non-fixed LPR system. Therefore, the horizontal and vertical dilation, skew angle detection and automatic bounding box have been proposed to detect the license plate. The proposed method has been applied to the four different types of Myanmar license plates, e.g., private cars, taxi, tour buses and religion cars. One car each is taken into four different types of angles on the dissimilar conditions. Experimental result indicated that this method can detect the disparate types of license plates with a high accuracy, i.e., the proposed approach achieved a favorable outcome rate of 97% at 100 license plates.

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Section: Related Workmentioning

confidence: 99%

License Plate Detection of Myanmar Vehicle Images from Dissimilar Angle Conditions

Khin

Phothisonothai

Choomchuay

2017

2017 21st International Computer Science and Engineering Conference (ICSEC)

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“…Horak research declares Harris, Eigen and FAST methods have a high productivity value in detecting the number plate location [5]. This is because the theoretical background is relatively focused on the angle.…”

Section: Introductionmentioning

confidence: 99%

Detection of Indonesian Vehicle Plate Location using Harris Corner Feature Detector Method

Akbar¹,

Maulana²

2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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Intelligent transportation systems are now starting to develop. One aspect of the intelligent transportation support system is how to recognize a vehicle. Vehicle plate recognition is an important function of this system. Plate detection systems have two problems, namely where the plate is located and how large the size is. Harris, Eigen and FAST methods have a high production value in detecting the number plate location. This is because the theoretical background is relatively focused on the angle. The Harris Corner detection system is often used because it can produce consistent values in rotating image and which has a lot of noise in an image. Morphological filters openings and closings can be seen as transformations with a structuring element which locally adapts its shape to the image structures, and therefore have lovely filtering capabilities. In this paper, we present the Indonesian plate localization based on text segmentation of unstructured standard plates with morphology filters openings and closings on pre-processing and feature detector Harris Corner method. Harris Corner method is used to detect corner points of the text, as well as using the Euclidean Distance algorithm to match features. The proposed result method produces an accuracy of 98.98%, a precision of 65.57% and a recall value of 73.55%. For further research, it needs to be compared with other methods to get the best results.

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