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

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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2

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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3

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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Qin, Shiyu. "Technological Development and Application of Image Recognition - License Plate Recognition." Applied and Computational Engineering 121, no. 1 (2025): 109–15. https://doi.org/10.54254/2755-2721/2025.19738.

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License Plate Recognition has become popular due to the advancement of technologies in traffic and vehicle management, law enforcement, and smart city development. Artificial intelligence and image recognition are two fundamental aspects behind the technical development of IR and its use in LPR. Image recognition using AI helps to develop a learning model that gets improved with time and more data processing. However, due to the nature of applications of LPR in real-life scenarios, accuracy and data security have become two critical challenges. The aim of the research is to explore whether adv

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12

Tseng, Hung Li, Chao Nan Hung, Chiu Ching Tuan, You Ru Wen, Wen Tzeng Huang, and Chin Hsing Chen. "Dynamic License Plate Localization for Vehicles on Multi-lane Using Single Camera." Applied Mechanics and Materials 300-301 (February 2013): 740–45. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.740.

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LPR (License Plate Recognition) System has been widely used in highway toll collection, parking management, various traffic regulations enforcement and other systems. Currently, most of the existing LPL (license plate localization) systems are with single camera that is limited to recognizing vehicles in one lane. In this paper we design a license plate localization system that simultaneously recognizes license plates of vehicles on multi-lane by using single high-resolution camera. Our approach significantly reduces the hardware cost of LPR system without sacrificing the accuracy of recogniti

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13

Abd Alhamza, Dunya A., and Ammar D. Alaythawy. "IRAQI LICENSE PLATE RECOGNITION BASED ON MACHINE LEARNING." Iraqi Journal of Information & Communications Technology 3, no. 4 (2020): 1–10. http://dx.doi.org/10.31987/ijict.3.4.94.

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The license plate recognition (LPR) is an important system. LPR is helpful in many ranges such as private or public entrance, parking lots, traffic control and theft surveillance. This paper, offers (LPR) consist of four main stages (preprocessing, license plate detection, segmentation, character recognition) the first stage takes a photo by the camera then preprocessing in this image. License plate detection search for matching of license plate in the image to crop the correct plate. Segmentation performed by divide the numbers separately. The last stage is number recognition by using KNN (K-

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14

Chen, Hongru, Yumin Lin, and Tianhao Zhao. "Chinese License Plate Recognition System Based on Convolutional Neural Network." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 95–102. http://dx.doi.org/10.54097/hset.v34i.5386.

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License Plate Recognition (LPR) technology has been developed for many years, but for the time being, LPR technology still has problems such as not being accurate enough in positioning and too long recognition time. Especially in China, the License Plate (LP) is made up of Chinese characters, alphabets, and numbers, in which the use of Chinese characters profoundly influences the accuracy of LPR. In this paper, a Convolutional Neural Network (CNN) is employed in LPR system, and the LPR system designed in this paper includes three parts: coarse LP positioning, precise LP positioning, and LP cha

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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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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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Md. Yusuf Ali, Nazmul Haque, Sumaiya Afrose Suma, and Md. Hadiuzzaman. "Video Sensor-Based Automatic License Plate Recognition of Static and Moving Vehicles." International Research Journal of Innovations in Engineering and Technology 06, no. 08 (2023): 25–30. http://dx.doi.org/10.47001/irjiet/2022.608004.

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In Bangladesh, present transportation system needs suitable control schemes to ensure safety and mobility to the road users. License Plate Recognition (LPR) is one of the control schemes which can be used to ensure road safety using video sensors. Moreover, from installation point of view, it is cheaper than Radiofrequency Identification (RFID) system. Automated LPR is adopted to reduce human involvement which causes loss in accuracy and reliability in reading license plate. The extracted information from LPR can be used in various schemes, such as electronic payment system, traffic surveillan

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Alharbi, Fares, Mohammed Zakariah, Reem Alshahrani, Ashwag Albakri, Wattana Viriyasitavat, and Abdulrahman Abdullah Alghamdi. "Intelligent Transportation Using Wireless Sensor Networks Blockchain and License Plate Recognition." Sensors 23, no. 5 (2023): 2670. http://dx.doi.org/10.3390/s23052670.

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License Plate Recognition (LPR) is essential for the Internet of Vehicles (IoV) since license plates are a necessary characteristic for distinguishing vehicles for traffic management. As the number of vehicles on the road continues to grow, managing and controlling traffic has become increasingly complex. Large cities in particular face significant challenges, including concerns around privacy and the consumption of resources. To address these issues, the development of automatic LPR technology within the IoV has emerged as a critical area of research. By detecting and recognizing license plat

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18

Zhu, Liping, Shang Wang, Chengyang Li, and Zhongguo Yang. "License Plate Recognition in Urban Road Based on Vehicle Tracking and Result Integration." Journal of Intelligent Systems 29, no. 1 (2019): 1587–97. http://dx.doi.org/10.1515/jisys-2018-0446.

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Abstract Multiple surveillance cameras provide huge video resources that need further mining to collect traffic stream data such as license plate recognition (LPR). However, these surveillance cameras have limited spatial resolution, which may not always suffice to precisely recognize license plates by existing LPR systems. This work is focused on the LPR method in low-quality images from surveillance video screenshots on urban road. The methodology we proposed is based on vehicle tracking and result integration, and we recognize the plate with an end-to-end method without character segmentati

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19

Raza, Muhammad Ali, Chun Qi, Muhammad Rizwan Asif, and Muhammad Armoghan Khan. "An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model." Applied Sciences 10, no. 6 (2020): 2165. http://dx.doi.org/10.3390/app10062165.

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License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment a

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20

Aboura, Khalid, and Rami Al-Hmouz. "An Overview of Image Analysis Algorithms for License Plate Recognition." Organizacija 50, no. 3 (2017): 285–95. http://dx.doi.org/10.1515/orga-2017-0014.

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Abstract Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most

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Zhang, Yesheng, Zilei Wang, and Jiafan Zhuang. "Efficient License Plate Recognition via Holistic Position Attention." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3438–46. http://dx.doi.org/10.1609/aaai.v35i4.16457.

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License plate recognition (LPR) is a fundamental component of various intelligent transportation systems, and is always expected to be accurate and efficient enough in real-world applications. Nowadays, recognition of single character has been sophisticated benefiting from the power of deep learning, and extracting position information for forming a character sequence becomes the main bottleneck of LPR. To tackle this issue, we propose a novel holistic position attention (HPA) in this paper that consists of position network and shared classifier. Specifically, the position network explicitly e

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Sultan, Fahd, Khurram Khan, Yasir Ali Shah, Mohsin Shahzad, Uzair Khan, and Zahid Mahmood. "Towards Automatic License Plate Recognition in Challenging Conditions." Applied Sciences 13, no. 6 (2023): 3956. http://dx.doi.org/10.3390/app13063956.

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License plate recognition (LPR) is an integral part of the current intelligent systems that are developed to locate and identify various objects. Unfortunately, the LPR is a challenging task due to various factors, such as the numerous shapes and designs of the LPs, the non-following of standard LP templates, irregular outlines, angle variations, and occlusion. These factors drastically influence the LP appearance and significantly challenge the detection and recognition abilities of state-of-the-art detection and recognition algorithms. However, recent rising trends in the development of mach

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Jain, Geerisha. "Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing." International Journal of Innovative Technology and Exploring Engineering 11, no. 12 (2022): 67–75. http://dx.doi.org/10.35940/ijitee.l9342.11111222.

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In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, came

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Shajan Jacob, M.K Jeyakumar. "Advanced License Plate Recognition with Squeezenet Effeciency." Journal of Information Systems Engineering and Management 10, no. 2 (2025): 607–23. https://doi.org/10.52783/jisem.v10i2.2454.

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License Plate Recognition (LPR) holds immense importance within the world of Intelligent Transportation Systems (ITS) due to its diverse applications. This study explores the implementation of a SqueezeNet model for LPR, addressing the critical role of intelligent transportation systems. By utilizing deep learning (DL) and image processing techniques, the purpose of the study is to enhance the accuracy and efficiency of automatic license plate recognition (ALPR). The characters on license plates are identified and recognized from a variety of vehicle images using the SqueezeNet architecture, w

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Nurhaida, Ida, Imam Nududdin, and Desi Ramayanti. "Indonesian license plate recognition with improved horizontal-vertical edge projection." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 811–21. https://doi.org/10.11591/ijeecs.v21.i2.pp811-821.

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License plate recognition (LPR) is one of the classical problems in the field of object recognition. Its application is very crucial in the automation of transportation system since it helps to recognise a vehicle identity, which information is stored in the license plate. LPR usually consists of three major phases: pre-processing, license plate localisation, optical character recognition (OCR). Despite being classical, its implementation faced with much more complicated problems in the real scenario. This paper proposed an improved LPR algorithm based on modified horizontal-vertical edge proj

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Geerisha, Jain. "Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 12 (2022): 67–75. https://doi.org/10.35940/ijitee.L9342.11111222.

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<strong>Abstract:</strong> In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the variou

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Gayathri, G. Roopa. "Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47103.

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Abstract: This study benchmarks probabilistic deep learning methods for license plate recognition (LPR), focusing on enhancing accuracy and reliability under real-world conditions. Utilizing a dataset of license plate images, the approach includes comprehensive preprocessing steps such as resizing, normalization, augmentation, and super-resolution to handle low-quality inputs. The dataset is split into training, validation, and testing subsets, with the test set emphasizing out-of-distribution (OOD) scenarios. The system employs convolutional neural networks (CNNs), probabilistic models like S

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Nurhaida, Ida, Imam Nududdin, and Desi Ramayanti. "Indonesian license plate recognition with improved horizontal-vertical edge projection." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 811. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp811-821.

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<p>License plate recognition (LPR) is one of the classical problems in the field of object recognition. Its application is very crucial in the automation of transportation system since it helps to recognise a vehicle identity, which information is stored in the license plate. LPR usually consists of three major phases: pre-processing, license plate localisation, optical character recognition (OCR). Despite being classical, its implementation faced with much more complicated problems in the real scenario. This paper proposed an improved LPR algorithm based on modified horizontal-vertical

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G., Kannan. "License Plate Recognition Using Undecimated Wavelet Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 556–60. https://doi.org/10.11591/ijeecs.v9.i3.pp556-560.

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License Plate Recognition (LPR) is the mission of identifying the vehicle using number plate extraction. An efficient method for recognizing plate based on Undecimated Wavelet Transform (UWT) is proposed. Plates are recognized using features from undecimated coefficients in this system. Morphological edge detection technique is used to get accurate results after feature extraction. Finally detected images are used for classification purpose using the feature coefficients. This technique is applied to all the unidentified and training images, extracted features are used as input to Back Propaga

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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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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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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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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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Cahyadi, Nanang, Sherfina Salsabila, Sevierda Raniprima, and Vivi Monita. "Review of License Plate Recognition Techniques with Deep Learning." Jurnal Teknologika 14, no. 2 (2024): 458–69. https://doi.org/10.51132/teknologika.v14i2.415.

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This article reviews vehicle license plate recognition (LPR) using deep learning techniques, which have become essential in intelligent transportation systems, law enforcement, and parking management. Deep learning, particularly Convolutional Neural Networks (CNNs), has replaced traditional methods with more accurate and robust systems capable of handling diverse real-world conditions. The article explores various deep learning approaches in LPR, including fusion, two-stage, end-to-end, multi-branch, and generative methods. Fusion methods combine deep learning with traditional image processing

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Hidayah, Maulidia Rahmah, Isa Akhlis, and Endang Sugiharti. "Recognition Number of The Vehicle Plate Using Otsu Method and K-Nearest Neighbour Classification." Scientific Journal of Informatics 4, no. 1 (2017): 66–75. http://dx.doi.org/10.15294/sji.v4i1.9503.

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The current topic that is interesting as a solution of the impact of public service improvement toward vehicle is License Plate Recognition (LPR), but it still needs to develop the research of LPR method. Some of the previous researchs showed that K-Nearest Neighbour (KNN) succeed in car license plate recognition. The Objectives of this research was to determine the implementation and accuracy of Otsu Method toward license plate recognition. The method of this research was Otsu method to extract the characteristics and image of the plate into binary image and KNN as recognition classification

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Kannan, G. "License Plate Recognition Using Undecimated Wavelet Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (2018): 558. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp558-560.

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<p>License Plate Recognition (LPR) is the mission of identifying the vehicle using number plate extraction. An efficient method for recognizing plate based on Undecimated Wavelet Transform (UWT) is proposed. Plates are recognized using features from undecimated coefficients in this system. Morphological edge detection technique is used to get accurate results after feature extraction. Finally detected images are used for classification purpose using the feature coefficients. This technique is applied to all the unidentified and training images, extracted features are used as input to Bac

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35

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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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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Ning, Yuan, Yao Wen Liu, Yan Bin Zhang, and Hao Yuan. "Research and Realization for Embedded License Plate Recognition System." Applied Mechanics and Materials 411-414 (September 2013): 1015–19. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1015.

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In this paper, the embedded license plate recognition system based on TMS320DM642 is researched. During the design, median filter, threshold, and morphology closing operations are used to obtain license plate region, then segmented into disjoint characters for the character recognition phase, where the template matching is used to identify the characters. Embedded License Plate Recognition System, being smaller, has less power consumption with respect to software based LPR systems. The resulting hardware is suitable for applications where cost, compactness, and efficiency are system design con

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Qu, Zhong, Qing-li Chang, Chang-zhi Chen, and Li-dan Lin. "An Improved Character Recognition Algorithm for License Plate Based on BP Neural Network." Open Electrical & Electronic Engineering Journal 8, no. 1 (2014): 202–7. http://dx.doi.org/10.2174/1874129001408010202.

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License plate character recognition is the basis of automatic license plate recognition (LPR) and it plays an important role in LPR. In this paper, we considered the advantages and disadvantages of the neural network method and proposed an improved approach of character recognition for license plates. In our approach, firstly, license plates were segmented into character pictures by using the algorithm which combines the projection and morphology. Secondly, with a focus on each character picture, recognition results determined by the calculation of the new recognition algorithm were as a refle

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A.R., Syafeeza, P. Marzuki, Asar Khan, Norihan Abdul Hamid, Wira Hidayat Mohd Saad, and Airuz Sazura A. Samad. "Enhanced Malaysian License Plate Recognition System Using an Improved YOLOv2 Model." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 16, no. 3 (2024): 35–39. http://dx.doi.org/10.54554/jtec.2024.16.03.005.

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License Plate Recognition (LPR) has gained popularity among researchers due to its wide range of applications, including law enforcement, monitoring, and toll gate systems. However, existing LPR systems still require improvements to achieve optimum accuracy and speed. The advancements in Convolutional Neural Network (CNN) variants offer potential solutions for these challenges. This primary aim of this system is to ensure accurate and efficient recognition of the vehicle plate characters using CNN techniques. This research utilizes two CNN network architectures for deep object detection to add

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Muhammad Sajid, Sadia Latif, Rana Muhammad Nadeem, Aafia Latif, and Muhammad Hassnain Azhar. "The Temporal Robustness of Classification Algorithms: Investigating the Impact of Temporal Changes on Model Performance." Kashf Journal of Multidisciplinary Research 2, no. 03 (2025): 151–64. https://doi.org/10.71146/kjmr350.

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Classifiers are the main source of processing of identification application task, So the performance of classifiers effect the work of any application. In this paper, author is working in the Digital Image Processing (DIP) domain, In License Plate Recognition (LPR) application of it. The purpose of this paper is, to introduce systemic literature review on why classification algorithms don’t work effectively after some period of time in some countries. Which decrease the performance of classifiers while processing License Plate Recognition (LPR) application or any identification application. Re

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Qin, Zhen Tao, Wu Nian Yang, and Ru Yang. "Research and Design of the License Plate Recognition Systems Based on ARM S3C2440." Applied Mechanics and Materials 333-335 (July 2013): 2484–88. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.2484.

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In order to meet the need of real-time and dynamic monitoring of intelligent transportation, a License Plate Recognition (LPR) System Based on ARM S3C2440 is introduced and a vehicle license recognition system is designed and realized. This thesis comparatively explains the tasks and problems and dose analytic research across all phases of the system. Image binary and slant rectification also be discussed, which are difficulty points in LPR. According to the study of the license plate images, we use hough transformation and image reverse rotation , a inclined rectification method was proposed.

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Nascimento, Valfride, Gabriel E. Lima, Rafael O. Ribeiro, William Robson Schwartz, Rayson Laroca, and David Menotti. "Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark." Journal of the Brazilian Computer Society 31, no. 1 (2025): 435–49. https://doi.org/10.5753/jbcs.2025.5159.

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Recent advancements in super-resolution for License Plate Recognition (LPR) have sought to address challenges posed by low-resolution (LR) and degraded images in surveillance, traffic monitoring, and forensic applications. However, existing studies have relied on private datasets and simplistic degradation models. To address this gap, we introduce UFPR-SR-Plates, a novel dataset containing 10,000 tracks with 100,000 paired low and high-resolution license plate images captured under real-world conditions. We establish a benchmark using multiple sequential LR and high-resolution (HR) images per

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Leng, Jiancai, Xinyi Chen, Jinzhao Zhao, et al. "A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing." Sensors 23, no. 21 (2023): 8913. http://dx.doi.org/10.3390/s23218913.

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With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance o

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Lee, Sung-Jin, Jun-Seok Yun, Eung Joo Lee, and Seok Bong Yoo. "HIFA-LPR: High-Frequency Augmented License Plate Recognition in Low-Quality Legacy Conditions via Gradual End-to-End Learning." Mathematics 10, no. 9 (2022): 1569. http://dx.doi.org/10.3390/math10091569.

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Scene text detection and recognition, such as automatic license plate recognition, is a technology utilized in various applications. Although numerous studies have been conducted to improve recognition accuracy, accuracy decreases when low-quality legacy license plate images are input into a recognition module due to low image quality and a lack of resolution. To obtain better recognition accuracy, this study proposes a high-frequency augmented license plate recognition model in which the super-resolution module and the license plate recognition module are integrated and trained collaborativel

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Navjeevan Chaudhary and Sunil Kumar S. Manvi. "Licence Plate Detection Using Machine Learning." Journal of Advanced Zoology 44, S6 (2023): 1090–95. http://dx.doi.org/10.17762/jaz.v44is6.2363.

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License Plate Recognition (LPR) is one of the tough tasks in the field of computer vision. Although it has been around for quite a while, there still lies the challenges when we have to deal with; the harsh environmental conditions like snowy, rainfall, windy, low light conditions etc. as well as the condition of the plates which includes the bent, rotated, broken plates. The performance of the recognition and detection frameworks take a significant hit when it is concerned with these conditional effects on the license plate. In this paper, we introduced a model to improve our accuracy based o

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Hsiao, Chao-Hsiang, Hoi Lee, Yin-Tien Wang, and Min-Jie Hsu. "Efficient License Plate Alignment and Recognition Using FPGA-Based Edge Computing." Electronics 14, no. 12 (2025): 2475. https://doi.org/10.3390/electronics14122475.

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Efficient and accurate license plate recognition (LPR) in unconstrained environments remains a critical challenge, particularly when confronted with skewed imaging angles and the limited computational capabilities of edge devices. In this study, we propose a high-performance, FPGA-based license plate alignment and recognition (LPAR) system to address these issues. Our LPAR system integrates lightweight deep learning models, including YOLOv4-tiny for license plate detection, a refined convolutional pose machine (CPM) for pose estimation and alignment, and a modified LPRNet for character recogni

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Putra, Firnanda Al-Islama Achyunda, Andriyan Rizki Jatmiko, Devita Maulina Putri, and Ardhillah Habibi Al-Fath. "Vehicle Licence Number Plate Recognition Using Convolution Neural Network for Traffic Violators in Indonesia." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 9, no. 2 (2024): 181–86. http://dx.doi.org/10.25139/inform.v9i2.8449.

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In the context of rising traffic violations and the need for efficient traffic management, this study explores the application of CNN in the recognition of licence plates to identify traffic violators in Indonesia. Traditional traffic enforcement methods are labour-intensive and prone to human error, necessitating a more automated and reliable approach. This research aims to enhance the accuracy and efficiency of license plate recognition (LPR) systems. The proposed system involves capturing vehicle images from the Roboflow Universe collected in the Malang area for use. We also use a CNN model

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Hussein, Khalid Ali, and Ziad Tariq A. Al-Ani. "Iraqi License Plate Recognition Based on Neural Network Technique." Journal of Physics: Conference Series 2322, no. 1 (2022): 012025. http://dx.doi.org/10.1088/1742-6596/2322/1/012025.

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Abstract License Plate (LP) is the unique identification of a vehicle. License Plate Recognition (LPR) is considered as one of the promising aspects of applying computer seeing technology across intelligent transportation system. We used mathematical morphology and edge detection as a method for segmentation and extracting the vehicle license plate character, in Location of the vehicle plate. Initially, the color image was changed to a gray image, and by calculating the difference between the pixels and their neighbors in order to build the edge of the image. This makes the license panel appea

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Kamal, Nada N., and Enas Tariq. "License Plate Tilt Correction: A Review." Engineering and Technology Journal 39, no. 1B (2021): 101–16. http://dx.doi.org/10.30684/etj.v39i1b.1839.

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Tilt correction is an essential step in the license plate recognition system (LPR). The main goal of this article is to provide a review of the various methods that are presented in the literature and used to correct different types of tilt that appear in the digital image of the license plates (LP). This theoretical survey will enable the researchers to have an overview of the available implemented tilt detection and correction algorithms. That’s how this review will simplify for the researchers the choice to determine which of the available rotation correction and detection algorithms to imp

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Gao, Fei, Yichao Cai, Yisu Ge, and Shufang Lu. "EDF-LPR: a new encoder–decoder framework for license plate recognition." IET Intelligent Transport Systems 14, no. 8 (2020): 959–69. http://dx.doi.org/10.1049/iet-its.2019.0253.

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Liu, Lei, Qiang Wei, and Xiao Ling Song. "Study on License Plate Recognition System based on Hybrid Programming of VC++ and MATLAB." Advanced Materials Research 403-408 (November 2011): 1712–15. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1712.

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Application of License plate recognition system(LPR) in intelligent transportation is discussed in this paper, and various practical recognition algorithm is analyzed. VC++ with a good interface of MPC's and the Matlab which have powerful and fast graphics image processing functions are introduced. A novel method combining the VC++ and Matlab is designed to complete the recognition of License Plate Recognition. Some experiments are made to validate the effectiveness of the proposed method. The results show that the real time of the algorithm is enhanced. The mean processing period of a plate i

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