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Статті в журналах з теми "License Plate Recognition System"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Wang, Rui Feng, Xiao Jin Fu, and Wei Xu. "License Plate Recognition System Design." Applied Mechanics and Materials 738-739 (March 2015): 639–42. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.639.

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Анотація:

The license plate recognition system is an important part of modern traffic management. application which is very extensive. In this paper, a method to achieve three main modules split from the image pre-processing, license plate location and character. Image pre-processing module of this article is to image gray and step by Roberts operator edge detection. License plate positioning and segmentation using mathematical morphology is used to determine the license plate location method, and then use the license plate color information of color segmentation method to complete the license plate par

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2

Li, Bo, Zhi Yuan Zeng, Hua Li Dong, and Xiao Ming Zeng. "Automatic License Plate Recognition System." Applied Mechanics and Materials 20-23 (January 2010): 438–44. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.438.

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This paper proposed an algorithm for license plate recognition system(LPRS). The vertical edge was first detected by sobel color edge detector. Then, the invalid edge was removed regarding edge density. Next, the license plate(LP) image was converted into HSV color model, and by edge density template and fuzzy color information judgement, the LP region was located. Then, color-reversing judgement and tilt correction was conducted. Afterward, characters were segmented by means of vertical projection and convolution, by which character width and position can be exactly confirmed, and character r

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3

Vishakha, Hanumant Jagtap, Vikas Dhotre Rohit, Rajendra Khandare Utkarsh, Narayan Khuspe Harshada, and Kokare Rohini. "Automatic license plate recognition system." ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA 10, no. 48 (2024): 01–06. http://dx.doi.org/10.5935/jetia.v10i48.955.

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4

NAIDU, T. D. V. A. "Automatic License Plate Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44494.

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Анотація:

Automatic license plate recognition using Image processing offer students a unique opportunity to gain hands-on experience in designing and optimizing the Embedded system. This typically involve working with industry professionals on actual projects, providing interns with valuable exposure to real-world challenges and best practices in the field of Embedded system. During the project, participants are often tasked with designing using specialized software tools like MATLAB. In recent times, the number of vehicles on road has exponentially risen due to which traffic congestion and violations a

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5

Ning, Yuan, Yao Wen Liu, Yan Bin Zhang, and Hao Yuan. "Extraction of License Plate Region in License Plate Recognition System." Applied Mechanics and Materials 441 (December 2013): 655–59. http://dx.doi.org/10.4028/www.scientific.net/amm.441.655.

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Анотація:

Extraction of License plate region is an important stage in the intelligent vehicle license plate recognition system. A practical license plate extraction algorithm based on edge detection and mathematical morphology is presented, the algorithm mainly consists of six modules: pre-processing, edge detection, binaryzation and denoising, morphology operation, filtration of connected regions, finding license plate region. From the experiments, the algorithm can detect the region of license plate quickly with 98% average accuracy of locating vehicle license plate region.

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6

Neves, Júlio da Silva, Maria Eduarda Silva Werlang, Victor Rafael Ferreira de Roma, Andreza Maria de Souza Rocha, and Jeferson Roberto de Lima. "SR'S: License plate recognition." EnGeTec em Revista 1, no. 1 (2024): 76–84. https://doi.org/10.5281/zenodo.10905888.

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Анотація:

This article aims to address the growing problem arising from the exponential increase in the number of vehicles in circulation around the world, which is resulting in increasingly complex challenges about urban traffic. To solve this pressing issue, a project called the Security Recognition Service (SR’s) was conceived. This project proposes automated solutions through the implementation of a device equipped with an advanced license plate recognition system. This device is complemented by a highly specialized data management system, specifically tailored to meet the needs of condominium

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7

Sukaina, Sh Altyar, Shams Hussein Samera, and Ahmed Tawfeeq Lubab. "Accurate license plate recognition system for different styles of Iraqi license plates." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 1092~1102. https://doi.org/10.11591/eei.v12i2.4186.

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Automatic license plate recognition (ALPR) used for many applications especially in security applications, including border control. However, more accurate and language-independent techniques are still needed. This work provides a new approach to identifying Arabic license plates in different formats, colors, and even including English characters. Numbers, characters, and layouts with either 1-line or 2-line layouts are presented. For the test, we intend to use Iraqi license plates as there is a wide range of license plate styles written in Arabic, Kurdish, and English/Arabic languages, each d

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8

Sh Altyar, Sukaina, Samera Shams Hussein, and Lubab Ahmed Tawfeeq. "Accurate license plate recognition system for different styles of Iraqi license plates." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 1092–102. http://dx.doi.org/10.11591/eei.v12i2.4186.

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Анотація:

Automatic license plate recognition (ALPR) used for many applications especially in security applications, including border control. However, more accurate and language-independent techniques are still needed. This work provides a new approach to identifying Arabic license plates in different formats, colors, and even including English characters. Numbers, characters, and layouts with either 1-line or 2-line layouts are presented. For the test, we intend to use Iraqi license plates as there is a wide range of license plate styles written in Arabic, Kurdish, and English/Arabic languages, each d

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9

TARNG, WERNHUAR, and CHIEN-LUNG LI. "ENHANCING THE ACCURACY OF LICENSE PLATE RECOGNITION SYSTEMS WITH THE ANGLE RECOVERY METHOD." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 08 (2013): 1350025. http://dx.doi.org/10.1142/s0218001413500250.

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Анотація:

The motor vehicle is an important way of transportation for modern people, and its license plate is just like our identification cards which can be used for effective management of motor vehicles. Hence, the development of a recognition system for license plates can reduce the workload of managing motor vehicles. A license plate recognition system based on computer vision often causes recognition errors due to the plate's angle problem and thus needs to be assisted by manual recognition. In this study, a recovery method for license plate images with large angles is proposed based on perspectiv

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10

Aarti, Soni* Dr.Raman Chadha Sukhmeet Kaur. "A REVIEW PAPER ON RECOGNIZE AUTOMATIC NUMBER PLATE AND BLURRED NUMBER PLATES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 719–24. https://doi.org/10.5281/zenodo.51910.

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Анотація:

This review paper provides a brief survey on various recognition techniques for automatic number plate recognition (ANPR) in image processing. ANPR is real –time embedded system which uses number plate to identify the vehicle. This expertise is in advance popularity in security and traffic installations. License plate recognition system is an application of computer vision. Computer vision is a method of using a computer to take out high level information from a digital image. The useless homogeny among different license plates such as its dimension and the outline of the license plate.

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11

Mudda, Avinash, P. Sashi Kiran, Ashish Kumar, and Venkata Sreenivas. "Vehicle Allowance System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1085–89. http://dx.doi.org/10.22214/ijraset.2023.50169.

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Анотація:

Abstract: License plate detection is an image processing technology that uses a license (number) plate for vehicle identification. The objective is to design and implement an efficient vehicle identification system that identifies the vehicle using the vehicle’s license plate. The system can be implemented at the entrance of parking lots, toll booths, or any private premises like colleges, etc. to keep records of ongoing and outgoing vehicles. It can be used to allow access to only permitted vehicles inside the premises. The developed system first captures the image of the vehicle’s front, the

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12

Teng, Xiu Hua. "The Application of Image Processing Technology in the Intelligent Transportation System." Applied Mechanics and Materials 543-547 (March 2014): 2678–80. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2678.

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Image processing-based vehicle recognition is one of the important research fields in ITS. The existing methods are all based on license plate recognition and car shape recognition. Their common problem is algorithm stability. And the license plates are easy to be changed. All information about vehicles should be used to recognize them reliably. A problem to be solved is to find a method to recognize vehicles besides license plate recognition and vehicle model recognition. Vehicle license plate location and character segmentation are critical steps in the license plate recognition system, and

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13

Zack, Lan, and Agnes Evert. "License Plate Recognition System Using MATLAB." DJ Journal of Advances in Electronics and Communication Engineering 1, no. 1 (2015): 29–33. http://dx.doi.org/10.18831/djece.org/2015011005.

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14

El-said, Shaimaa Ahmed. "Shadow aware license plate recognition system." Soft Computing 19, no. 1 (2014): 225–35. http://dx.doi.org/10.1007/s00500-014-1245-5.

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15

Randive, P. S., Sonam Bansod, Shruti Ahivale, Sonal Mohite, and Sneha Patil. "Automatic License Plate Recognition [ALPR] System." International Journal of Engineering Trends and Technology 35, no. 5 (2016): 224–27. http://dx.doi.org/10.14445/22315381/ijett-v35p248.

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16

Tiruneh, Embiale Merkebu, and De Ning Jiang. "Vehicle License Plate Registration Recognition System." Advanced Materials Research 718-720 (July 2013): 2286–90. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2286.

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Анотація:

Neural network had been used widely in many applications, such as to recognize an object or character, to detect a motion, to control a process, to forecast a result, to analyze data and for management of information. With the rapid growth of vehicles on the road and with the aid of improved technology, there is a demand for processing vehicles as conceptual resources in information systems. This paper will show how to design a system using the neural network to recognize the vehicle registration plate of vehicles. The approach to the project is by capturing footage and after which, the footag

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17

Ben Laoula, El Mehdi, Omar Elfahim, Marouane El Midaoui, Mohamed Youssfi, and Omar Bouattane. "Multi-agent cloud based license plate recognition system." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 4590. http://dx.doi.org/10.11591/ijece.v14i4.pp4590-4601.

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Анотація:

This paper presents a multi-agent license plate recognition system, specifically designed to address the diverse and challenging nature of license plates. Utilizing a multi-agent architecture with agents operating in individual Docker containers and orchestrated by Kubernetes, the system demonstrates remarkable adaptability and scalability. It leverages advanced neural networks, trained on a comprehensive dataset, to accurately identify various license plate types under dynamic conditions. The system’s efficacy is showcased through its three-layered approach, encompassing data collection, proc

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18

Yamini, Maidam. "Number Plate Detection in an Image." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25883.

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Анотація:

Automatic Vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this ar

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19

Mehdi, Ben Laoula El, Omar Elfahim, Midaoui Marouane El, Mohamed Youssfi, and Omar Bouattane. "Multi-agent cloud based license plate recognition system." Multi-agent cloud based license plate recognition system 14, no. 4 (2024): 4590–601. https://doi.org/10.11591/ijece.v14i3.pp4590-4601.

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Анотація:

This paper presents a multi-agent license plate recognition system, specifically designed to address the diverse and challenging nature of license plates. Utilizing a multi-agent architecture with agents operating in individual Docker containers and orchestrated by Kubernetes, the system demonstrates remarkable adaptability and scalability. It leverages advanced neural networks, trained on a comprehensive dataset, to accurately identify various license plate types under dynamic conditions. The system’s efficacy is showcased through its three-layered approach, encompassing

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20

Tawfeeq, Furat, and Yasmine Tabra. "Gate Control System for New Iraqi License Plate." Iraqi Journal for Computers and Informatics 41, no. 1 (2014): 1–3. http://dx.doi.org/10.25195/ijci.v41i1.89.

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This paper presents an approach to license plate localization and recognition. A proposed method is designed to control the opening of door gate based on the recognition of the license plates number in Iraq. In general the system consists of four stages; Image capturing, License plate cropping, character segmentation and character recognition. In the first stage, the vehicle photo is taken fromstandard camera placed on the door gate with a specific distance from the front of vehicle to be processed by our system. Then, the detection method searches for the matching of the license plate in the

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21

Pan, Shenghu, Jian Liu, and Dekun Chen. "Research on License Plate Detection and Recognition System based on YOLOv7 and LPRNet." Academic Journal of Science and Technology 4, no. 2 (2023): 62–68. http://dx.doi.org/10.54097/ajst.v4i2.3971.

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With the development and continuous iteration of digital transformation and artificial intelligence technology, the license plate detection and recognition system based on traditional machine vision and the current deep learning-based license plate recognition system for China is unable to meet the needs of rapid and accurate real-time recognition and recognition in complex environments. This paper designs and integrates a set of license plate detection and recognition system based on YOLOv7, STN and LPRNet models, which can recognize Chinese license plates quickly and accurately in real time,

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22

Pekh, P., and V. Hrygorychenko. "Development of the system for recognition of car license plate using artificial intelligence." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 57 (February 13, 2025): 20–25. https://doi.org/10.36910/6775-2524-0560-2024-57-03.

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Анотація:

The article proposes a technology for creating a license plate recognition system using artificial intelligence. The license plate recognition program is developed in the PyCharm integrated development environment (IDE), which supports Python and provides access to libraries for machine learning and image processing. Libraries for working with images and text, such as OpenCV, imutils, pytesseract, and others, are installed through the PyCharm terminal. For the processing and recognition of license plates, a set of images containing various car license plates was prepared. These images are made

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23

Yuan, Shuai, Guo Yun Zhang, Jian Hui Wu, and Long Yuan Guo. "Study of License Plate Recognition Technology." Advanced Materials Research 834-836 (October 2013): 1035–38. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1035.

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Анотація:

License plate recognition technology has been widely used with the development of intelligent traffic system, which studies vehicle identification based on digital image processing technology. This paper presents system design and realization of recognition system for license plate. License plate image is preprocessed by gradation and binaryzation at first, then the image noise caused by dirt is filtered by a mean value method. We adopt horizontal and vertical projection method to locate license plate. Character segmentation and recognition are carried out at last. Test result shows that the m

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24

Ran, Feng, Fa Yu Zhang, and Mei Hua Xu. "Research and Design of License Plate Recognition System." Applied Mechanics and Materials 556-562 (May 2014): 2623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2623.

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Introduce a complete system of license plate recognition: using morphological processing and priori knowledge of license plate to discern the location of license plate, accomplishing tilt correction through Radon transform, then fulfilling character segmentation of accurate positioning license plate by projection, finishing character recognition through BP neural network which was improved by the use of adaptive learning rate and momentum factor. With the programming and verification on Matlab experimental platform, experimental results show that we can have a preferable recognition speed and

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25

Ma, Zhen, Jian Lei Li, and Xue Fei Tan. "Research on License Plate Recognition Technology." Applied Mechanics and Materials 44-47 (December 2010): 3667–71. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3667.

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Анотація:

With the growth in number of vehicle, intelligent vehicle management has become the research hotspot, and license plate automatic recognition system is also of great concern as a important technology of intelligent traffic system. In this paper, Hough transform used to edge extraction, tilt correction algorithm and character recognition based on Haursdorff distance are discussed. At last, the license plate recognition system is designed and implemented, and then the experimental result is analyzed.

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26

Bin Mohamad Azhar, Muhammad Darwish, Kah Ong Michael Goh, Law Check Yee, and Tee Connie. "A Robust License Plate Detection System Using Smart Device." JOIV : International Journal on Informatics Visualization 8, no. 2 (2024): 931. http://dx.doi.org/10.62527/joiv.8.2.2287.

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Анотація:

The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license pla

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27

Naaz, Humera. "Automatic License Number Plate Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51094.

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Анотація:

The rapid growth of urban transportation networks has increased the need for automated vehicle identification systems to support traffic management, law enforcement, and public safety. This paper presents a robust Automatic License Number Plate Recognition (ALNPR) framework that uses OpenCV for image processing and PyTesseract for character extraction. The system processes both video and image inputs to detect license plates, extract alphanumeric data, and store it in an SQLite database with timestamps. Emphasis is placed on real-time performance, modular architecture, and adaptability to diff

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28

Lee, Jae-Hyeon, Sung-Man Cho, Seung-Ju Lee, Cheong-Hwa Kim, and Goo-Man Park. "License Plate Recognition System Using Synthetic Data." Journal of the Institute of Electronics and Information Engineers 57, no. 1 (2020): 107–15. http://dx.doi.org/10.5573/ieie.2020.57.1.107.

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29

Azad, Babak, and Eslam Ahmadzade. "Real-Time Multiple License Plate Recognition System." International Journal of Research in Computer Science 4, no. 2 (2014): 11–17. http://dx.doi.org/10.7815/ijorcs.42.2014.080.

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30

Rajput, Hitesh, Tanmoy Som, and Soumitra Kar. "An Automated Vehicle License Plate Recognition System." Computer 48, no. 8 (2015): 56–61. http://dx.doi.org/10.1109/mc.2015.244.

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31

Bhogale, Poonam, Archit Save, Vitrag Jain, and Saurabh Parekh. "Vehicle License Plate Detection and Recognition System." International Journal of Computer Applications 137, no. 9 (2016): 31–34. http://dx.doi.org/10.5120/ijca2016908924.

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32

Saha, Satadal, Subhadip Basu, and Mita Nasipuri. "iLPR: an Indian license plate recognition system." Multimedia Tools and Applications 74, no. 23 (2014): 10621–56. http://dx.doi.org/10.1007/s11042-014-2196-7.

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33

Mohta, Amogh, Anand Swaroop, Katkuri Fhanindra Reddy, and Manjula R. "Automatic License Plate Recognition System Using YOLOv4." International Research Journal on Advanced Science Hub 5, Issue 05S (2023): 280–86. http://dx.doi.org/10.47392/irjash.2023.s038.

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34

Ginting, Rudolf F. A., Julaica F. Djawas, and Yampi R. Kaesmetan. "Pengenalan Plat Kendaraan Otomatis Berbasis Citra Menggunakan Metode Optical Character Recognition (OCR)." Journal Software, Hardware and Information Technology 4, no. 2 (2024): 11–17. http://dx.doi.org/10.24252/shift.v4i2.135.

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Automatic parking registration has become a focal point in the research and development of modern urban transportation systems. In this context, image-based vehicle license plate recognition plays a vital role in facilitating efficient and accurate parking registration processes. This research aims to develop an image-based vehicle license plate recognition system for automatic parking registration applications using Optical Character Recognition (OCR) technology. The methods employed include vehicle image acquisition, image preprocessing, license plate segmentation, text extraction using OCR,

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35

Kwon, Hyun, and Jang-Woon Baek. "Adv-Plate Attack: Adversarially Perturbed Plate for License Plate Recognition System." Journal of Sensors 2021 (November 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/6473833.

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Анотація:

Deep learning technology has been used to develop improved license plate recognition (LPR) systems. In particular, deep neural networks have brought significant improvements in the LPR system. However, deep neural networks are vulnerable to adversarial examples. In the existing LPR system, adversarial examples study specific spots that are easily identifiable by humans or require human feedback. In this paper, we propose a method of generating adversarial examples in the license plate, which has no human feedback and is difficult to identify by humans. In the proposed method, adversarial noise

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36

Yaba, Hawar Hussein, and Hemin Omer Latif. "Plate Number Recognition based on Hybrid Techniques." UHD Journal of Science and Technology 6, no. 2 (2022): 39–48. http://dx.doi.org/10.21928/uhdjst.v6n2y2022.pp39-48.

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Анотація:

Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world an

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37

Lin, Cheng-Jian, Chen-Chia Chuang, and Hsueh-Yi Lin. "Edge-AI-Based Real-Time Automated License Plate Recognition System." Applied Sciences 12, no. 3 (2022): 1445. http://dx.doi.org/10.3390/app12031445.

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Анотація:

The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server for license plate recognition. To reduce delays and bandwidth use during image transmission, this study proposes an edge-AI-based real-time ALPR (ER-ALPR) system, in which an AGX XAVIER embedded system is embedded on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time aut

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38

Liu, Zhong Yan, Jian Yang, and Hong Mei Nie. "An Efficient Algorithm for License Plate Recognition." Applied Mechanics and Materials 278-280 (January 2013): 1297–300. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1297.

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Анотація:

The license plate recognition(LPR) is the key technology in intelligent transportation system. This paper discusses the whole process of license plate recognition technology, include the license plate image preprocessing, license plate location, character segmentation and character recognition, and simulated it by MATLAB. The experimental result show this method can obtain good recognition effect.

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39

Wang, Ye Qin, Liang Hai Chen, and Li Yun Zhuang. "Research on License Plate Recognition System Based on Computer Vision." Applied Mechanics and Materials 65 (June 2011): 536–41. http://dx.doi.org/10.4028/www.scientific.net/amm.65.536.

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In order to achieve the exact location and character recognition of license plate, firstly, this paper got binary image of license plate and done edge detection with differential operation. Secondly, it searched the license plate binary image after difference for the horizontal and vertical cut point, and determined the best cutting threshold through the experiment. Finally, it made the character segmentation by vertical projection, the recognition of license plate characters with the use of BP neural network, whose overall recognition rate is at 95.3%, and the display interface design for pro

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40

Yang, Bintao. "Studies Advanced in License Plate Recognition." Applied and Computational Engineering 8, no. 1 (2023): 512–18. http://dx.doi.org/10.54254/2755-2721/8/20230257.

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License plate recognition is a crucial mission in computer vision, and deep learning has significantly improved its performance. A representative license plate recognition system involves five components: license plate image preprocessing, image acquisition, license plate detection, character recognition, and character segmentation. This paper will explore the methods commonly used in each stage of the recognition process and analyze some of the current challenges and future trends of license plate recognition. This discussion will consider real-world factors such as lighting and weather condi

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41

Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library an

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Hu, Zhong Hua, and Chen Tang. "Research of License Plate Recognition System Based on Labview." Applied Mechanics and Materials 734 (February 2015): 646–49. http://dx.doi.org/10.4028/www.scientific.net/amm.734.646.

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The vehicle license plate recognition system is the intelligent traffic management system based on the image and the character recognition technology, which is an important part of the intelligent transportation system. This paper introduces a method of vehicle license plate location based on edge detection and morphological operations, virtual instrument is combined with machine vision of the license plate recognition method [1]. Finally the license plate number of the vehicle is get. Experiment results show that such method can simplify the algorithm and has some correct location rate.

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43

.Mamatha, Mrs B. "Revolutionizing Toll Collection with Automatic Number Plate Detection Systems." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50483.

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Abstract—One kind of security system is the number plate recognition system. The NPR s ystem uses image processing principles. Additionally, this makes use of an OCR (Optical Character Recognition) system to decipher images of license plates. Tollway authorities utilize number plate recognition systems for a variety of reasons, one of which is to automatically recognize a vehicle's license plate, give the driver with a pay-slip, and then open the toll road to that vehicle. To authorize the car to park in their designated location, parking authorities also use this technique. The technology wor

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44

Mhatre, Amruta, Prashant Sharma, and Anup R. Maurya. "Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 10–20. http://dx.doi.org/10.17762/ijritcc.v11i9.8112.

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An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide rang

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S.Mahalakshmi and Dheeba J. Dr. "Robust Approach of Automatic Number Plate Recognition System using Deep CNN." JXU Journal, Scopus Indexed 50, no. 3 (2023): 1–5. https://doi.org/10.5281/zenodo.8382498.

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<strong>Automatic License plate / Number plate / Registration plate recognition is recognized as an automation which evolved mostly based on image processing techniques. It has been extensively used in recognizing vehicles in applications such as red-light enforcement, over speeding, parking control, toll collection. The main objective of the paper is to identify the most well-planned way to identify the registration plate from the digital image (gained from the camera)and recognize with high accuracy. ANPR is employed to localize the license plates, segment each character and extract the text

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Singh, Astha, and Pawan Singh. "License Plate Recognition for Traffic Management." Journal of Management and Service Science (JMSS) 1, no. 2 (2021): 1–14. http://dx.doi.org/10.54060/jmss/001.02.001.

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The objective of this report is to present an overview of the project, license plate recognition. This system is to basically detect a vehicle using the information of the license plate in order to use the information at various valid sites. This tool can work as a part of other big projects in the industry for security purposes as well as for analysis purposes. There is a detailed insight of the project in several different chapters throughout the report. This project is based on detection and recognition algorithms; using several libraries of python to work on images and videos and thereafte

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Kuchuk, Heorhii, Andrii Podorozhniak, Nataliia Liubchenko, and Daniil Onischenko. "System of license plate recognition considering large camera shooting angles." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (November 29, 2021): 82–91. http://dx.doi.org/10.32620/reks.2021.4.07.

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The system of automatic license plate recognition (ALPR) is a combination of software and hardware technologies implementing ALPR algorithms. It seems to be easy to achieve the goal but recognition of license plate requires many difficult solutions to some non-trivial tasks. If the license plate is oriented horizontally, uniformly lighted, has a clean surface, clearly distinguishable characters, then it’ll be not too difficult to recognize such a license plate. However, the reality is much worse. The lighting of each part of the plate isn’t equal; the picture from the camera is noisy. Besides,

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Yang, Xing, Yong Shun Ling, Xiao Li Hao, Hua Yang, and Peng Ma. "Anti-Alteration Technology for License Plate Recognition System." Advanced Materials Research 211-212 (February 2011): 156–60. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.156.

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In order to realize anti-alteration function for License Plate Recognition System (LPRS), a uniform-field imaging system is designed and a corresponding anti-alteration algorithm is proposed. First, reflection characteristics of license plate and typical alteration material are measured. As a result, the two characteristics in near-infrared range fluctuate moderately and the former is notably lower than the latter. Then the uniform-field imaging system for visible-light and near-infrared is designed to capture the difference above effectively. Finally, the anti-alteration algorithm, composed o

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Kounlaxay, Kalaphath, Yeo Chan Yoon, and Soo Kyun Kim. "Vehicle License Plate Detection and Recognition using OpenCV and Tesseract OCR." International Journal on Advanced Science, Engineering and Information Technology 14, no. 4 (2024): 1170–77. http://dx.doi.org/10.18517/ijaseit.14.4.18137.

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License plate recognition (LPR) is essential as the number of vehicles increases and the human ability to accomplish this task is limited. If human labor is used to manage these, it will take a lot of time and energy and cause a discrepancy. License Plate Recognition (LPR) is an advanced technology that leverages optical character recognition (OCR) and various image processing methods to read vehicle license plates automatically. Typically, an LPR system comprises two primary components: detecting vehicles and their license plates and recognizing the alphanumeric characters displayed on those

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Wady, Shakhawan H., Faraidoon H. Ahmad, and Hawkar O. Ahmed. "Iraqi Kurdistan Vehicle License Plate Recognition System based on Client-server Network." Journal of Zankoy Sulaimani - Part A 19, no. 1 (2016): 251–62. http://dx.doi.org/10.17656/jzs.10603.

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