Relevant bibliographies by topics / License Plate Recognition (LPR)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'License Plate Recognition (LPR)'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "License Plate Recognition (LPR)"

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M. Merola, Linda, Cynthia Lum, Breanne Cave, and Julie Hibdon. "Community support for license plate recognition." Policing: An International Journal of Police Strategies & Management 37, no. 1 (2014): 30–51. http://dx.doi.org/10.1108/pijpsm-07-2012-0064.

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Purpose – Although the use of license plate recognition (LPR) technology by police is becoming increasingly common, no empirical studies have examined the legal or legitimacy implications of LPR. LPR may be used for a variety of purposes, ranging from relatively routine checks of stolen vehicles to more complex surveillance functions. The purpose of this paper is to develop a “continuum of LPR uses” that provides a framework for understanding the potential legal and legitimacy issues related to LPR. The paper then analyzes results from the first random-sample community survey on the topic. Des
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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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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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Zheng, Yujie, Lei Guan, and Haohong Li. "The Low-light License Plate Recognition via CNN." Journal of Physics: Conference Series 2424, no. 1 (2023): 012028. http://dx.doi.org/10.1088/1742-6596/2424/1/012028.

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Abstract The low-light license plate recognition (LPR) is an important task in LPR, and the task of low-light LPR is a challenge in LPR. Compared with ordinary LPR, low-light LPR is more challenging. The first is that there are few studies on low-light LPR, and there is a lack of dedicated datasets. Besides, there are few lightweight networks dedicated to low-light LPR. The lack of lightweight private networks makes it difficult to deploy LPR methods efficiently. Based on this, this paper proposes a low-light LPR method. Specifically, we propose a dataset dedicated to low-light LPR with a samp
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Sharma, Niresh, and Varsha Namdeo. "An Efficient and Robust Multi Directional Deep Learning Based Licence Plate Recognition." International Journal of Membrane Science and Technology 10, no. 2 (2023): 2151–63. http://dx.doi.org/10.15379/ijmst.v10i2.2783.

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Smart cities must have all the important characteristics to achieve their intended goals. Proper traffic management and controlling, increased surveillance and safety, and enhanced management and avoidance of incidents must be the priorities of smart cities. Meanwhile, license plate recognition (LPR) has become the most debatable topic in the research community due to various real-time applications, such as “law enforcement, toll-free processing, access control, and traffic surveillance.” Automated LPR is a technique based on computer vision to recognize vehicles with their number plates. This
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Wang, Hanxiang, Yanfen Li, L. Minh Dang, and Hyeonjoon Moon. "Robust Korean License Plate Recognition Based on Deep Neural Networks." Sensors 21, no. 12 (2021): 4140. http://dx.doi.org/10.3390/s21124140.

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With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include
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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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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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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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Yaacob, Nur Liyana, Ammar Ahmed Alkahtani, Fuad M. Noman, Ahmad Wafi Mahmood Zuhdi, and Dhuha Habeeb. "License plate recognition for campus auto-gate system." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 128–36. https://doi.org/10.11591/ijeecs.v21.i1.pp128-136.

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Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to ex
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Liyana Yaacob, Nur, Ammar Ahmed Alkahtani, Fuad M. Noman, Ahmad Wafi Mahmood Zuhdi, and Dhuha Habeeb. "License plate recognition for campus auto-gate system." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 128. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp128-136.

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<p><span>Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this p
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CHEN, ZHENXUE, FALIANG CHANG, and CHUNSHENG LIU. "CHINESE LICENSE PLATE RECOGNITION BASED ON HUMAN VISION ATTENTION MECHANISM." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 08 (2013): 1350024. http://dx.doi.org/10.1142/s0218001413500249.

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License plate recognition (LPR) is one of the most important elements affecting intelligent transportation systems. A number of LPR techniques have been proposed. Humans are good target recognition systems. In other words, humans easily recognize common objects. In this paper, the researchers present a novel method of recognizing Chinese license plates. The method is based on the Human Vision Attention Mechanism (HVAM) and uses Chinese license plates as the targets. The research consists of three stages. The first stage involved finding and identifying license plates in videos of moving vehicl
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Dissertations / Theses on the topic "License Plate Recognition (LPR)"

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Liaqat, Ahmad Gull. "Mobile Real-Time License Plate Recognition." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-15944.

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License plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring, expressway toll system, electronic-police system, and security systems. In recent years, there has been a lot of research in license plate recognition, and many recognition systems have been proposed and used. But these systems have been developed for computers. In this project, we developed a mobile LPR system for Android Operating System (OS). LPR involves three main components: license plate detection, character segmentation a
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Kazantzoglou, Avraam. "Flexible Architecture System & Topology License Plate Recognition (FAST LPR) and Concept of Operations in Thailand." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Sept/08Sep%5FKazantzoglou.pdf.

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Thesis (M.S. in Electronic Warfare Systems Engineering and M.A. in Security Studies (Stabilization and Reconstruction))--Naval Postgraduate School, September 2008.<br>Thesis Advisor(s): Sankar, Pat ; McNab, Robert. "September 2008." Description based on title screen as viewed on November 6, 2008. Includes bibliographical references (p. 151-154). Also available in print.
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Girjotas, Andrius. "Transporto priemonių numerių atpažinimo algoritmų analizė bei universalios atpažinimo sistemos teorija." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20140702_193526-52512.

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Automatinis transporto priemonių registracijos numerio atpažinimas atlieka svarbų vaidmenį daugelyje programinių paketų, taikomų tiek valstybinėse institucijose, tiek ir privačiose kompanijose, kuriuose yra naudojamos įvairios atpažinimo algoritmų technologijos. Tačiau net ir dabar neįmanoma sukurti idealiai veikiančios sistemos, kuri palieka laisvę efektyviausių algoritmų paieškai. Šio tiriamojo darbo tikslas yra išanalizuoti alternatyvius automobilio numerio lokalizacijos ir kitų atpažinimo etapų algoritmus, jų efektyvumą bei adaptyvumą. Analizė atliekama juos realizuojant ir atliekant tyrim
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Kazlauskas, Tomas. "Transporto priemonių numerių atpažinimo algoritmų analizė bei universalios atpažinimo sistemos teorija." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20140702_193534-66751.

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Automatinis transporto priemonių registracijos numerio atpažinimas atlieka svarbų vaidmenį daugelyje programinių paketų, taikomų tiek valstybinėse institucijose, tiek ir privačiose kompanijose, kuriuose yra naudojamos įvairios atpažinimo algoritmų technologijos. Tačiau net ir dabar neįmanoma sukurti idealiai veikiančios sistemos, kuri palieka laisvę efektyviausių algoritmų paieškai. Šio tiriamojo darbo tikslas yra išanalizuoti alternatyvius automobilio numerio lokalizacijos ir kitų atpažinimo etapų algoritmus, jų efektyvumą bei adaptyvumą. Analizė atliekama juos realizuojant ir atliekant tyrim
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Songke, Li, and Chen Yixian. "License plate recognition." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9442.

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This paper presents a method for license plate recognition through analysis of vehicle images. Accurate location of license plate and extracting characters of plate are implemented. In the plate location module, the paper puts forward arithmetic of plate edge recognition by morphology algorithms. Meanwhile, Radon transform is used to adjust different angles between viewer and license plate. At last, characters extraction is done by histogram. And the extraction characters are matched with the templates. Experimental results show that this approach can recognize license plates more effectively
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Ning, Guanghan. "Vehicle license plate detection and recognition." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157318.

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<p> In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process. </p><p> Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We teste
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ALVARENGA, EDUARDO PIMENTEL DE. "OPTICAL CHARACTER RECOGNITION FOR AUTOMATED LICENSE PLATE RECOGNITION SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28690@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Sistemas de reconhecimento automático de placas (ALPR na sigla em inglês) são geralmente utilizados em aplicações como controle de tráfego, estacionamento, monitoração de faixas exclusivas entre outras aplicações. A estrutura básica de um sistema ALPR pode ser dividida em quatro etapas principais: aquisição da imagem, localização da placa em uma foto ou frame de vídeo; segmentação dos caracteres que compõe a placa; e reconhecimento destes car
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Ho, Wai Yiu. "License Plate Recognition algorithms and their application to Macao license plates." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182850.

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Gunaydin, Ali Gokay. "A Constraint Based Real-time License Plate Recognition System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608195/index.pdf.

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License Plate Recognition (LPR) systems are frequently utilized in various access controls and security applications. In this thesis, an experimental constraint based real-time License Plate Recognition system is designed, and implemented in Java platform. Many of the available constraint based methods worked under strict restrictions such as plate color, fixed illumination and designated routes, whereas, only the license plate geometry and format constraints are used in this developed system. These constraints are built on top of the current Turkish license plate regulations. The plate locali
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Chow, Jiunn Nan, and 周俊男. "License Plate Recognition System." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/35737856446114458856.

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碩士<br>國立中山大學<br>資訊工程研究所<br>83<br>In this thesis, we develop a license plate recognition system. The system first automatically locate the location of license plate ,and then use the mathematical morphology to do recognition. Traditional character recognition systems use segmentation, thinning, stroke detection and so on. From the strokes relationship, they use a similarity measure to select best matched character. In this thesis, we use the mathematical morphology method to extract the prop
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Books on the topic "License Plate Recognition (LPR)"

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Sahoo, Santosh Kumar. Real-Time Implementation of License Plate Recognition (Lpr) System. GRIN Verlag GmbH, 2018.

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Shaheen, Iyad. Car Radar System,from Design to License Plate Recognition. Lulu Press, Inc., 2012.

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Shah, Rajiv, and Brendan Mcquade. Surveillance, Security, and Intelligence-Led Policing in Chicago. Edited by Larry Bennett, Roberta Garner, and Euan Hague. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040597.003.0012.

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This chapter summarizes the Chicago Police Department’s adoption of Intelligence-Led Policing (ILP) since the early-2000s as a crime prevention and deterrence strategy. It reviews the use of technology such as police observation devices (cameras), the centralization of the Police Department’s data operations at the Crime Prevention and Information Center, a sophisticated data analytics “fusion center,” and examines changing technologies of surveillance used by the police. The authors discuss the integration of police surveillance with privately-owned and operated camera systems, and explore ho
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Book chapters on the topic "License Plate Recognition (LPR)"

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Asaju, Christine Bukola, Pius Adewale Owolawi, Chunling Du, and Etienne Van Wyk. "Enhancing Security with Automated Boom Gate Access Through License Plate Recognition Utilising YOLOv8 Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85856-7_15.

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Abstract The combination of computer vision and deep learning approaches has changed automated systems across numerous domains. Such a domain is object detection. This study presents an automatic boom gate access method based on the YOLOv8 (You Only Look Once version 8) object detection model and license plate recognition (LPR) technology. It tries to resolve the issue of secure and efficient boom gate entry in restricted regions. The approach takes advantage of YOLOv8’s capacity to reliably detect and recognize license plates in real-time, allowing for automated gate operation. The experiment
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Tang, Dongqi, Hao Kong, Xi Meng, Ruo-Ze Liu, and Tong Lu. "SEE-LPR: A Semantic Segmentation Based End-to-End System for Unconstrained License Plate Detection and Recognition." In MultiMedia Modeling. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37731-1_44.

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Gezahegn, Yaecob Girmay, Misgina Tsighe Hagos, Dereje H. Mariam W. Gebreal, Zeferu Teklay Gebreslassie, G. agziabher Ngusse G. Tekle, and Yakob Kiros T. Haimanot. "Intelligent License Plate Recognition." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95153-9_25.

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Indira, K., K. V. Mohan, and Theegalapally Nikhilashwary. "Automatic License Plate Recognition." In Recent Trends in Signal and Image Processing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8863-6_8.

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Burry, Aaron, and Vladimir Kozitsky. "Automated License Plate Recognition." In Computer Vision and Imaging in Intelligent Transportation Systems. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118971666.ch2.

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Nobile, Nicola, Hoi Kei Phoebe Chan, and Marleah Blom. "A Comprehensive Unconstrained, License Plate Database." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_48.

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Shen, Ren-Chao, and Day-Fann Shen. "License Plate Recognition Under Nonuniform Illumination." In Intelligent Technologies and Engineering Systems. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6747-2_24.

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Garibotto, Giovanni, Paolo Castello, and Enrico Ninno. "Dynamic Vision for License Plate Recognition." In Multimedia Video-Based Surveillance Systems. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4327-5_23.

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Wu, Yue, and Jianmin Li. "License Plate Recognition Using Deep FCN." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5230-9_25.

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Caccia, Fabio, Roberto Marmo, and Luca Lombardi. "License Plate Detection and Character Recognition." In Image Analysis and Processing – ICIAP 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_51.

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Conference papers on the topic "License Plate Recognition (LPR)"

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Singh, Er Inderjeet, Parvez Rahi, Vanshika Jain, Nidhi Sharma, Yuvraj Anand, and Ashutosh Kumar Shukla. "Recognition System: Detection of License Plate." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673076.

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B S, Prajwal, Suraj B. Madagaonkar, and Krishnamoorthi Makkithaya. "License Plate Recognition Using Federated Learning." In 2024 Fourth International Conference on Multimedia Processing, Communication & Information Technology (MPCIT). IEEE, 2024. https://doi.org/10.1109/mpcit62449.2024.10892789.

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Zhang, Cong, Qi Wang, and Xuelong Li. "EQ-LPR: Efficient Quality-Aware License Plate Recognition." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191206.

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Nascimento, Valfride, Rayson Laroca, and David Menotti. "Super-Resolution Towards License Plate Recognition." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sibgrapi.est.2023.27448.

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Recent years have seen significant developments in license plate recognition through the integration of deep learning techniques and the increasing availability of training data. Nevertheless, reconstructing license plates from low-resolution surveillance footage remains challenging. To address this issue, we propose an attention-based super-resolution approach that incorporates sub-pixel convolution layers and an Optical Character Recognition (OCR)-based loss function. We trained the proposed architecture on synthetic images created by applying heavy Gaussian noise followed by bicubic downsam
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Nascimento, Valfride, Rayson Laroca, and David Menotti. "Super-Resolution Towards License Plate Recognition." In Concurso de Teses e Dissertações. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/ctd.2024.1999.

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Recent years have seen significant developments in license plate recognition through the integration of deep learning techniques and the increasing availability of training data. Nevertheless, reconstructing license plates from low-resolution surveillance footage remains a challenge. To address this issue, we propose an attention-based super-resolution approach that incorporates sub-pixel convolution layers and an Optical Character Recognition (OCR)-based loss function. We trained the proposed architecture using synthetic images created by applying heavy Gaussian noise followed by bicubic down
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Rahmani, Mahdi, Melika Sabaghian, Seyyedeh Mahila Moghadami, Mohammad Mohsen Talaie, Mahdi Naghibi, and Mohammad Ali Keyvanrad. "IR-LPR: A Large Scale Iranian License Plate Recognition Dataset." In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2022. http://dx.doi.org/10.1109/iccke57176.2022.9960129.

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Garg, Khushi, Kapil Gautam, Karamveer Singh, and Madhvi Gaur. "License Number Plate Recognition(LNPR)." In 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2022. http://dx.doi.org/10.1109/icac3n56670.2022.10074115.

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Dominguez, Leonardo, Juan Pablo D'Amato, Alejandro Perez, Aldo Rubiales, and Rosana Barbuzza. "Running license plate recognition (LPR) algorithms on smart survillance cameras. A feasibility analysis." In 2018 13th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2018. http://dx.doi.org/10.23919/cisti.2018.8399194.

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Ali, Farheen, Himanshu Rathor, and Wasim Akram. "License Plate Recognition System." In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2021. http://dx.doi.org/10.1109/icacite51222.2021.9404706.

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Renuka devi, D., and D. Kanagapushpavalli. "Automatic license plate recognition." In Computing (TISC). IEEE, 2011. http://dx.doi.org/10.1109/tisc.2011.6169088.

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Reports on the topic "License Plate Recognition (LPR)"

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Dennis, Frank. The Effect of Color on Character Recognition: A Study of the Oregon License Plate. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6460.

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Thomas, Michael J. Combining Facial Recognition, Automatic License Plate Readers and Closed Circuit Television to Create an Interstate Identification System for Wanted Subjects. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1009302.

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