Extraction of car license plate using motor vehicle regulation and character pattern recognition

Sirithinaphong, T.; Chamnongthai, Kosin

doi:10.1109/apccas.1998.743881

Cited by 15 publications

(4 citation statements)

References 4 publications

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“…2) ANNs: Multilayered feedforward neural networks are also used for LP character identification in many works [15], [19], [29], [79], [125]- [127], [132], [135], usually following a common methodology. The classical training method for feedforward neural networks is error backpropagation [118].…”

Section: Character Recognitionmentioning

confidence: 99%

License Plate Recognition From Still Images and Video Sequences: A Survey

Anagnostopoulos

Psoroulas

et al. 2008

IEEE Trans. Intell. Transport. Syst.

512

248

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License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.

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Section: Character Recognitionmentioning

confidence: 99%

License Plate Recognition From Still Images and Video Sequences: A Survey

Anagnostopoulos

Psoroulas

et al. 2008

IEEE Trans. Intell. Transport. Syst.

512

248

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show abstract

“…Its working principle is based on portable exhaust detection technology, and real-time measurement of vehicle exhaust and driving characteristics and other parameters [15]. The advantage of this method is that it can simulate real-time road conditions, and also can quickly obtain exhaust emission data of different sections of "different types of vehicles" in different periods [16]. On the premise of ensuring the accuracy and reliability of detection, it can also save a lot of time and samples [17].…”

Section: Introductionmentioning

confidence: 99%

Image Processing Technology in Emission of Intelligent Traffic Diesel Vehicle

2022

IJMC

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Traditional smoke measurement methods for diesel vehicles mainly include Lingman blackness telescope and smoke meter. The Lingman Blackness Telescope uses a binocular prism telescope system. On the telescope partition board, a gray scale step block with corresponding Lingman smoke concentration of 1-5 level is made, and the transparent part is O level. The observer compares the smoke target with the gray gradient block through the left eyepiece of the telescope to determine the standard grade of smoke blackness. Smoke is divided into sampling part and measuring part. In the sampling part, the sampling cylinder extracts a certain volume of smoke from the exhaust pipe so that it passes through a certain area of white filter paper, so the particulate matter in the exhaust gas adheres to the filter paper and makes the filter paper black. Then, the smoke traces on the filter paper are measured by photoelectric detection device to evaluate the exhaust smoke of diesel engine. The above method can measure the smoke level of diesel vehicle exhaust to a certain extent, but it has great application limitations in practice, or cannot be applied in practice. For example, (I) Although the level of diesel vehicle exhaust smoke can be measured with the Lingman blackness telescope, it cannot record the level of diesel vehicle exhaust smoke and the relevant information of the vehicle; (2) The Lingman blackness telescope must be measured manually. It is time-consuming and laborious, and it is impossible for traffic police to observe every vehicle 24 hours with Lingman blackness telescope; (3) Smokemeter can only measure static diesel vehicles, but cannot measure driving vehicles. In view of the above problems, this paper introduces digital image processing technology, through the analysis of digital image of diesel vehicle exhaust, realizes the dynamic detection of diesel vehicle exhaust.

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“…For character segmentation, several techniques have been developed such as projection [7,8] and connected component analysis [9,10]. For character recognition, several algorithms have also been used, such as template matching [11] and neural networks [12]. Among these, two literatures focused on solving Macao LPR problems.…”

Section: Introductionmentioning

confidence: 99%

An Edge-Based Macao License Plate Recognition System

Pun¹,

Ho²

2011

IJCIS

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This paper presents a system to recognize Macao license plates. Sobel edge detector is employed to extract the vertical edges, and an edge composition algorithm is proposed to combine the edges into candidate plate regions. They are further examined on the existence of the character "M" by a verification algorithm. A row separation algorithm is also proposed to cater both one-row and two-row types of plates. Projection analysis and template matching methods are exploited to segment and recognize the characters. Various pre and post processing steps are proposed other than traditional implementation so as to improve the recognition accuracy. This work achieves a high recognition rate of 95%.

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Extraction of car license plate using motor vehicle regulation and character pattern recognition

Cited by 15 publications

References 4 publications

License Plate Recognition From Still Images and Video Sequences: A Survey

License Plate Recognition From Still Images and Video Sequences: A Survey

Image Processing Technology in Emission of Intelligent Traffic Diesel Vehicle

An Edge-Based Macao License Plate Recognition System

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