A robust license-plate extraction method under complex image conditions

Kim, Sung Hoon; Kim, Daechul; Ryu, Younbok; Kim, Gyeonghwan

doi:10.1109/icpr.2002.1047833

Cited by 70 publications

(7 citation statements)

References 6 publications

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“…As part of the quality verification of the license plate recognition subsystem, it was determined that the installed fixed point cameras show high reliability of almost above 95% when using two cameras and 90% when using one camera. The reliability values are absolutely sufficient for systems of similar type, as also evidenced by publications focusing on tests by other authors [22][23][24][25]. In general, it is advisable to place the cameras with license plate detection as close as possible to the communication.…”

Section: Discussionmentioning

confidence: 83%

Sustainable Traffic Regulation System in Protected Areas: Pilot Technology Testing in National Park in the Czech Republic

Růžička,

Sliacky,

Purkrábková

et al. 2023

Sustainability

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In the context of nature protection, there is an effort to regulate individual car traffic in protected areas. In the framework of the research, a pilot testing of a vehicle detection and identification system in the Krkonoše National Park was carried out using two selected technologies (license plate recognition and Bluetooth token detection). The research was carried out under conditions of poorer availability of mobile signal for transmission of measured data, lack of electrical power supply, and in challenging climatic conditions in the mountains. The main objective was to verify the applicability and limits of the mentioned technologies under these difficult conditions. For this purpose, two test sites were built: a fixed and a mobile point. Testing at both points was carried out using two basic methods, namely online through continuous data collection from the detectors and on-site through a local survey during the summer of 2022. The parameters evaluated were the reliability of the vehicle identification itself and the reliability of the operation of the individual detection subsystems and the tested system as a whole. The results show that the license plate recognition system using two cameras for the checkpoint shows a high recognition reliability, but it is reduced for some types of vehicles (especially motorcycles and four-wheelers). At the same time, this technology is demanding on energy resources. Detection using a Bluetooth scanner has proven to be highly reliable up to 50 km/h. A reliable power supply is necessary to achieve high reliability, which was a problem at the mobile point. Evaluation of images from cameras with motion detection showed the limits of this technology, which increased with increasing vehicle speed. The system can be used to detect traffic in protected areas, taking into account the limits specified in this article.

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Section: Discussionmentioning

confidence: 83%

Sustainable Traffic Regulation System in Protected Areas: Pilot Technology Testing in National Park in the Czech Republic

Růžička,

Sliacky,

Purkrábková

et al. 2023

Sustainability

View full text Add to dashboard Cite

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“…[6], and two or more combined features [7], respectively. Using these methods, LPR systems detect LPs by finding a rectangle in the input [3][4] or by exploiting LP-related information such as connected objects [5] [8], colors [14] and characters [2] [6].…”

Section: Related Workmentioning

confidence: 99%

“…Segmented characters are then recognized by pixel comparison or by feature extraction method. Pixel comparison approaches can recognize characters fast, but they are susceptible to noise [3] [16]. On the other hand, feature extraction approaches are computation heavy, yet they are robust against noise [7] [19].…”

Section: Related Workmentioning

confidence: 99%

“…License plate recognition (LPR) systems are widely used and studied camera-based applications [1]. From 1970 to the present, extensive research on LPR has been developed and deployed to various environments employing several different LPR methods [2] [3][4] [5] [6]. Furthermore, recent advances in camera hardware now allow researchers and engineers to work with higher resolution inputs from wide angles to realize feature-rich automatic LPR systems [7] [8][9] [10].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Style License Plate Recognition System using K-Nearest Neighbors

2019

KSII TIIS

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There are various styles of license plates for different countries and use cases that require style-specific methods. In this paper, we propose and illustrate a multi-style license plate recognition system. The proposed system performs a series of processes for license plate candidates detection, structure classification, character segmentation and character recognition, respectively. Specifically, we introduce a license plate structure classification process to identify its style that precedes character segmentation and recognition processes. We use a K-Nearest Neighbors algorithm with pre-training steps to recognize numbers and characters on multi-style license plates. To show feasibility of our multi-style license plate recognition system, we evaluate our system for multi-style license plates covering single line, double line, different backgrounds and character colors on Korean and the U.S. license plates. For the evaluation of Korean license plate recognition, we used a 50 minutes long input video that contains 138 vehicles of 6 different license plate styles, where each frame of the video is processed through a series of license plate recognition processes. From two experiments results, we show that various LP styles can be recognized under 50 ms processing time and with over 99% accuracy, and can be extended through additional learning and training steps.

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“…These applications were used for identifying vehicles in a restricted area and for detecting stolen vehicles (Kranthi et al, 2011;Chenhong and Zhaoyang, 2008;Hongliang and Changping, 2004;Sunghoon et al, 2002;Sang et al, 1996). Recognition of license plates using traffic video databases was carried out on global edge features with various parameters based on blob detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition System of Vehicle Plate for Recording Incoming Vehicles to the University Campus and the Outgoing

El-Harby¹,

Behery²,

Al-Dhawi³

2017

American Journal of Applied Sciences

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Automatic recognition of vehicle plate characters became a very important in our daily life because of the unlimited increase of vehicles that enter or leave a supervised area. This paper describes an automatic intelligent system that captures the images of vehicles and has the ability to recognize the plates of vehicles entering the university campus or leaving. A camera is built on the university gate for taking images of incoming or outgoing vehicles. A rear image of a vehicle is captured and processed. The system applies an intelligent filtering of the input image based on a set of filters removing unnecessary image elements preserving the position and shape of characters of the vehicle plate, histogram manipulation and edge detection techniques for plate localization and characters segmentation. Adaptive Neuro Fuzzy Inference System (ANFIS) is chosen as a classifier for recognizing the characters in the vehicle plates. This system can extracts the letters and numbers from vehicles plates. So, the extracted vehicle characters can be used to record the incoming vehicles to the university campus and the outgoing. The recognized vehicle characters are saved in the university database.

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A robust license-plate extraction method under complex image conditions

Cited by 70 publications

References 6 publications

Sustainable Traffic Regulation System in Protected Areas: Pilot Technology Testing in National Park in the Czech Republic

Sustainable Traffic Regulation System in Protected Areas: Pilot Technology Testing in National Park in the Czech Republic

Multi-Style License Plate Recognition System using K-Nearest Neighbors

Automatic Recognition System of Vehicle Plate for Recording Incoming Vehicles to the University Campus and the Outgoing

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