Parallelogram and Histogram based Vehicle License Plate Detection

Deb, Kaushik; Chae, Hyun-Uk; Jo, Chae Kang-Hyun

doi:10.1109/icsma.2008.4505550

Cited by 20 publications

(7 citation statements)

References 7 publications

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“…A technique based on extract candidate regions by finding vertical and horizontal edges from vehicle region had also been proposed and this segmentation method is named as sliding concentric windows. 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.…”

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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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: 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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“…The global featured classifier decreases the complexity of the system.. The whole algorithm is described in [34].…”

Section: G Feature Based Techniques: A) Geometric Feature Based Techmentioning

confidence: 99%

“…The algorithm uses geometry based features to capture characteristics of License plate in the image regions [34]. Algorithm work as follows.…”

Section: G Feature Based Techniques: A) Geometric Feature Based Techmentioning

confidence: 99%

Algorithms for licenseplate detection: A survey

Farajian¹,

Rahimi²

2014

2014 International Congress on Technology, Communication and Knowledge (ICTCK)

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To identify a vehicle, License Plate identification technology is the key technology of intelligent transport system, finding a stolen vehicle, monitoring traffic flow, in town management, highway tolls etc. The major issue in such applications is to detect the exact location of plate, because finding the location of number plate could vary and as well as its size and color; many of the vehicles have an uncertain difference in License plate position, location and unclean License Plate. In this paper we have conducted a very fresh survey of most authentic techniques of license plate detection from a car, we studied different techniques and compared them; we figured out every technique has its own limitations, every method gives best results under some certain conditions so do bad results, we figured out all we need to do is to have locus of control, we shall control theenvironment we are working in, some techniques works good in dark light, some for bright light etc; every technique has its own parameters to identify license plate; this paper will lead you to choose the best technique for detection just according to your circumstance.

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“…Sliding concentric window (SCW) is used to identify vertical and horizontal edges of a grayscale image. The result is a binarized image with filtered non‐edges obtained by the ratio of the SCWs, based on a threshold value [15]. An OR operation is further used to mask both edges (vertical and horizontal) to produce regions with rectangular shape.…”

Section: Alpr On Microprocessor Platformmentioning

confidence: 99%

Automatic license plate recognition on microprocessors and custom computing platforms: A review

Princewill

Adetunmbi

Alese

et al. 2021

IET Image Processing

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Automatic license plate recognition (ALPR) is the process of extracting and recognizing character information within a localized license plate region. Typically, ALPR involves three steps; image capture, image procession and plate recognition. The performance of an ALPR is largely dependent on the quality of the captured image, which is determined by factors such as environmental variation, camera quality and occlusion. Image procession and plate recognition step involves image processing techniques that extract and recognizes license plate and characters, respectively. ALPR systems could be realized on microprocessors (software-based) or custom computing platforms (hardware-based). Drawbacks such as portability, power consumption and computational speed limit software-based ALPR for real-time deployment. Custom platforms for ALPR consume less power and achieve high processing speed for real-time capability. However, limited computing resources available within a custom chip make it difficult to implement State-of-the-Art computationally intensive algorithms. Thus, very few literatures discussed ALPR techniques on custom computing platforms. This paper presents a comprehensive review of algorithms and architectures of ALPR on microprocessors and custom computing platforms. Design approaches, performance, gaps, suggestions and trends are discussed.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Parallelogram and Histogram based Vehicle License Plate Detection

Cited by 20 publications

References 7 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

Algorithms for licenseplate detection: A survey

Automatic license plate recognition on microprocessors and custom computing platforms: A review

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