License Plate Recognition Based On Prior Knowledge

Gao, Qian; Wang, Xin‐Nian; Xie, Gongfu

doi:10.1109/ical.2007.4339089

Cited by 37 publications

(19 citation statements)

References 8 publications

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“…Constants on equations (8)(9)(10) are determined empirically. In our experiments, in 55 images the LP color is white; among them using those constants in 53 images candidate region was detected successfully.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, vehicle license plate is verified and detected by using HSI color model and position histogram, respectively [6]. Prior knowledge of LP and color collocation has been used to locate the license plate in the image [8] as part of the procedure of location and segmentation. The horizontal and vertical projections are scanned by using a search window to locate the license plate in [9].…”

Section: Review Of Other Methodsmentioning

confidence: 99%

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Statistical characteristics in HSI color model and position histogram based vehicle license plate detection

Deb

Kang

2009

Intel Serv Robotics

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Visual perception takes an important role in the implementation of intelligent robot and transportation systems. Such perception is to detect and recognize various objects in the real environment. Detecting license plate (LP) is a crucial and inevitable component of the vehicle license plate recognition (VLPR) system. In this proposed algorithm, initially, HSI color model is adopted to select automatically statistical threshold value for detecting candidate regions. According to different colored LP, these candidate regions may include LP regions; geometrical properties of LP are then used for classification. The proposed method is able to deal with candidate regions under independent orientation and scale of the plate. Finally, the decomposition of candidate regions contains predetermined LP alphanumeric character by using position histogram to verify and detect vehicle license plate (VLP) region. In experiment more than 150 images were used, and they were taken from the variety of conditions such as complex scenes, illumination changing, distances and varied weather etc. Under these conditions, success of LP detection has reached more than 94%.

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Section: Resultsmentioning

confidence: 99%

Section: Review Of Other Methodsmentioning

confidence: 99%

Statistical characteristics in HSI color model and position histogram based vehicle license plate detection

Deb

Kang

2009

Intel Serv Robotics

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show abstract

“…b) Character segmentation: A wide variety of techniques have been proposed to segment each character after plate localization. The method proposed in [118] used the dimensions of each character for fixed segmentations. Meanwhile, the structure of the Chinese license plate is used to construct a classifier for recognition.…”

Section: Vehicle Recognitionmentioning

confidence: 99%

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Tian

Morris

Tang

et al. 2017

IEEE Trans. Intell. Transport. Syst.

108

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Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.

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“…For character segmentation various attributes i.e. vertical and horizontal projection attributes [7,16], pixel connectivity [17,18], character contour feature [19], mathematical morphology [20] and characters prior knowledge [13,21] have been utilized. The segmentation accuracy has a great influence for proper recognition rate since majority of the recognition errors in VLPR framework occur because of the segmentation errors.…”

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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License Plate Recognition Based On Prior Knowledge

Cited by 37 publications

References 8 publications

Statistical characteristics in HSI color model and position histogram based vehicle license plate detection

Statistical characteristics in HSI color model and position histogram based vehicle license plate detection

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

A Vehicular License Plate Recognition Framework For Skewed Images

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