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Journal articles on the topic 'License plate classification'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Raghunandan, Karpuravalli Srinivas, Palaiahnakote Shivakumara, Lolika Padmanabhan, Govindaraju Hemantha Kumar, Tong Lu, and Umapada Pal. "Symmetry features for license plate classification." CAAI Transactions on Intelligence Technology 3, no. 3 (2018): 176–83. http://dx.doi.org/10.1049/trit.2018.1016.

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2

Xu, Tianqi, and Ziqi Zhang. "License plate classification based on MobileNetV2." Applied and Computational Engineering 4, no. 1 (2023): 171–78. http://dx.doi.org/10.54254/2755-2721/4/20230442.

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In China, there are various colors of license plates which represent different kinds of vehicles and we construct a model on Edge Impulse platform to deal with problems in actual engineering programs like parking, traffic flow control, city planning, etc. Lots of researchers have proposed different techniques about license plate classification but most of them have difficulties maintaining the balance between the size of the dataset and a decent accuracy. In order to achieve that, this research would combine transfer learning and MobileNetV2 to build a model on edge impulse. Transfer learning,

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3

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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Shu, Sen. "High Precision License Plate Recognition Algorithm in Open Scene." Journal of Physics: Conference Series 2560, no. 1 (2023): 012006. http://dx.doi.org/10.1088/1742-6596/2560/1/012006.

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Abstract At present, license plate recognition algorithm under restricted conditions is relatively mature and widely used in various license plate recognition system. Due to the influence of factors such as large differences in shooting angles and vehicle motion blur, Chinese license plate recognition is quite challenging. In response to the above problems, this research abandoned the single end-to-end deep learning license plate recognition method, and proposed a step-by-step license plate recognition algorithm that integrated detection and classification, and utilized a level-by-level object

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Singh, Siddhartha, Padmini Mishra, Siddhartha Ojha, Mohd Shoaib, Shivendra Kumar, and Vivek Kumar Yadav. "Helmet and License Plate Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3184–89. http://dx.doi.org/10.22214/ijraset.2023.52320.

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Abstract: Traffic accidents are one of the leading causes of death today. Motorcycle accidents lead to serious injuries. A helmet is important for every motorcyclist. However, many do not adhere to helmet laws. This is the software CNN uses to find motorcyclists without helmets. The structure consists of motorcycle detection, helmetless classification, and motorcycle licence plate recognition. The motorcycle is scanned with the feature vector HOG. Once the motorcycle is recognised by CNN, do motorcyclists wear helmets? Motorcycle licence plate if it is determined that the motorcyclist is not w

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Arslan, Gülistan, Fırat Aydemir, and Seyfullah Arslan. "Enhanced license plate recognition using deep learning and block-based approach." Journal of Scientific Reports-A, no. 058 (September 29, 2024): 57–82. http://dx.doi.org/10.59313/jsr-a.1505302.

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This study investigates the effectiveness of current deep learning techniques in license plate detection and makes essential contributions. Instead of classifying the characters on Turkish license plates with a single classifier, the characters are divided into blocks of numbers and letters using various image processing techniques, and a separate classifier is used for each block. The proposed approach improves character classification accuracy and license plate recognition accuracy. This approach eliminated the possibility of misclassifying similar letters and numbers and improved the charac

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Salsabila, Nurul, and Sriani Sriani. "Enhancing Automated Vehicle License Plate Recognition with YOLOv8 and EasyOCR." Journal of Information Systems and Informatics 6, no. 3 (2024): 1577–97. http://dx.doi.org/10.51519/journalisi.v6i3.848.

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This research focuses on the development of an automatic system for vehicle license plate recognition using YOLOv8, EasyOCR, and CNN methods for object classification. The main issue raised is the need for an accurate and efficient system for recognizing vehicle license plates in real-time in dynamic environments, especially in urban areas with high traffic levels. The method used in this study involves resizing the input image to 416x416 pixels to standardize the data, analyzing the YOLO architecture that divides the image into a 7x7 grid, and using the Convolutional Neural Network (CNN) algo

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Saidallah, Mustapha, Fatimazahra Taki, Abdelbaki El Belrhiti El Alaoui, and Abdeslam El Fergougui. "Classification and Comparison of License Plates Localization Algorithms." Signal & Image Processing : An International Journal 12, no. 2 (2021): 1–11. http://dx.doi.org/10.5121/sipij.2021.12201.

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The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the application of new information and communication technologies in the transport sector, to make the infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the key module of these systems, in which the License Plate Localization (LPL) is the most important stage, because it determines the speed and robustness of this module. Thus, during this step the algorithm must process the image and overcome several constraints as climatic and lighting co

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Purwanto, Wahyu, and Minda Septiani. "Implementasi Automatic License Plate Recognition untuk mengurangi pelanggaran lalu lintas berbasis Artificial Intelligence." INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics 8, no. 2 (2023): 148. http://dx.doi.org/10.51211/itbi.v8i2.2572.

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Abstract - Indonesia is a country with an increasing number of vehicles each year. However, with the growing traffic density in urban areas, traffic violations such as disregarding traffic signs and exceeding speed limits often occur. This research aims to implement the Automatic License Plate Recognition (ALPR) system using the K-Nearest Neighbors (KNN) algorithm to predict and recognize vehicle license plates in plate images. The research also aims to evaluate the accuracy level of the implemented ALPR system. The method used in this research is KNN, which is one of the classification method

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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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11

Uthaib, Masar Abed, and Muayad Sadik Croock. "Multiclassification of license plate based on deep convolution neural networks." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5266. http://dx.doi.org/10.11591/ijece.v11i6.pp5266-5276.

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In the classification of license plate there are some challenges such that the different sizes of plate numbers, the plates' background, and the number of the dataset of the plates. In this paper, a multiclass classification model established using deep convolutional neural network (CNN) to classify the license plate for three countries (Armenia, Belarus, Hungary) with the dataset of 600 images as 200 images for each class (160 for training and 40 for validation sets). Because of the small numbers of datasets, a preprocessing on the dataset is performed using pixel normalization and image data

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Masar, Abed Uthaib, and Sadik Croock Muayad. "Multiclassification of license plate based on deep convolution neural networks." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5266–76. https://doi.org/10.11591/ijece.v11i6.pp5266-5276.

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In the classification of license plate there are some challenges such that the different sizes of plate numbers, the plates' background, and the number of the dataset of the plates. In this paper, a multiclass classification model established using deep convolutional neural network (CNN) to classify the license plate for three countries (Armenia, Belarus, Hungary) with the dataset of 600 images as 200 images for each class (160 for training and 40 for validation sets). Because of the small numbers of datasets, a preprocessing on the dataset is performed using pixel normalization and image

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Sholehurrohman, Ridho, Kurnia Muludi, and Joko Triloka. "OPTIMIZING TRANSPORTATION SURVEILLANCE WITH YOLOV7: DETECTION AND CLASSIFICATION OF VEHICLE LICENSE PLATE COLORS." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 4 (2025): 812–20. https://doi.org/10.33480/jitk.v10i4.6260.

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Optimizing transportation surveillance requires accurate vehicle license plate color detection and classification; however, existing systems face significant challenges in achieving real-time accuracy and robustness, particularly in crowded traffic scenarios with varying lighting and plate conditions. In Indonesia, vehicle license plates are color-coded based on their usage, including white and black for private vehicles, yellow for public vehicles, red for government vehicles, and green for free-trade areas. Each plate color plays a crucial role in transportation management, enabling proper v

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Fitri, Damayant, Herawat Sri, Imamah, Ayu M. Fifin, and Rachmad Aeri. "Indonesian license plate recognition based on area feature extraction." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 620–27. https://doi.org/10.12928/TELKOMNIKA.v17i2.9017.

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The main principle of license plate recognition is to recognize the characters in the license plate which indicates the identity of the vehicle. This research will provide a system which can be implemented to the automatic payment in highway. Indonesian license plate consists of two parts, every of which has certain characters. These characters may become problem in the recognition process. Another problem is on the type of the license plate since Indonesia applies different color for every type of vehicle. In this research, different approaches are employed in the recognition of license plate

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15

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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16

Zakaria, Muhamad Rostan, Suhailan Safei, Wan Nural Jawahir Wan Yussof, and Sulidar Fitri. "Enhancing Parking Systems with QR Code-Integrated Automatic License Plate Recognition through Convolutional Neural Networks." Journal of Advanced Research Design 131, no. 1 (2025): 117–25. https://doi.org/10.37934/ard.131.1.117125.

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This abstract describes the development and evaluation of an Automatic License Plate Recognition (ALPR) system designed to simplify the process of parking ticket generation. The traditional paradigm of manual entry of license plate information by parking personnel for exiting vehicles is replaced by the automated system proposed in this study. The system integrates a YOLO (You Only Look Once) model for the automatic recognition of license plates in vehicle images. After this initial identification, a series of pre-processing and image segmentation techniques are applied to isolate and recogniz

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Adytia, Nico Ricky, and Gede Putra Kusuma. "Indonesian License Plate Detection and Identification Using Deep Learning." International Journal of Emerging Technology and Advanced Engineering 11, no. 7 (2021): 1–7. http://dx.doi.org/10.46338/ijetae0721_01.

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Abstract— License plate is the unique identity of the vehicle, which serves as proof of the legitimacy of the operation of the vehicle in the form of a plate or other material with certain specifications issued by the police and contains the area code, registration number and validity period and installed on the vehicle. License plates are often used in automated parking systems and vehicle identification in case of traffic violations. So, it is necessary to build a system for detection and identification of license plates. The proposed license plate detection and identification system is divi

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18

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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19

Swanand Joshi, Pramod Jejure, Chatrasal Jadhav, and Vishal Jankar. "Automatic Number Plate Recognition." International Journal of Scientific Research in Science and Technology 11, no. 5 (2024): 439–48. http://dx.doi.org/10.32628/ijsrst2411476.

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Automatic license plate recognition (ANPR) systems have become suitable for various applications, including traffic monitoring, law enforcement, and toll collection. This paper completes the study on automatic license plate recognition (ANPR) systems that use advanced imaging technology and machine learning algorithms to achieve accuracy in license plate verification and validation. The preparation process is adopted in various ways: image acquisition, preprocessing, location plate, character segmentation, and optical character recognition (OCR). The system, which integrates deep learning mode

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20

Salemdeeb, Mohammed, and Sarp Ertürk. "Full depth CNN classifier for handwritten and license plate characters recognition." PeerJ Computer Science 7 (June 18, 2021): e576. http://dx.doi.org/10.7717/peerj-cs.576.

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Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional lay

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Wndu Gata, Dwiza Riana, Muhammad Haris, Maria Irmina Prasetiyowati, and Dika Putri Metalica. "Automated Indonesian Plate Recognition: YOLOv8 Detection and TensorFlow-CNN Character Classification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 3 (2025): 474–83. https://doi.org/10.29207/resti.v9i3.6310.

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The precise identification and reading of Indonesian vehicle number plates are important in many areas, including the enforcement of law, collection of charges, management of parking areas, and safety measures. This study integrates the implementation of the YOLOv8 object detection algorithm with three OCR methods: EasyOCR, TesseractOCR, and TensorFlow. YOLOv8 is capable of identifying license plates from images and videos at a high speed and reliability under different conditions and therefore is used in this study to perform plate detection in images and videos. After licenses are detected,

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Hariono, Tholib. "IMPLEMENTASI SUPPORT VECTOR MACHINE UNTUK PENGENALAN PLAT NOMOR KENDARAAN." Exact Papers in Compilation (EPiC) 1, no. 3 (2019): 137–44. http://dx.doi.org/10.32764/epic.v1i3.156.

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License plate recognition recognition has a very important role in developing better transport systems such as electronic tolls, electronic parking, traffic monitoring activities and others. Number plate recognition process is carried out through four main stages (plate detection, segmentation, feature extraction and classification). Plate detection is done to obtain the location of license plate using the Viola Jones algorithm. The segmentation process to separate the characters from the image of the license plate area-based color using labeling techniques. Feature extraction is based on the

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Jafar Aty, Ahmed, and Jamshid B. Mohasefi. "Optimizing Car License Plate Recognition Through Gray Wolf Optimization Algorithm." Iraqi Journal for Electrical and Electronic Engineering 21, no. 2 (2025): 108–18. https://doi.org/10.37917/ijeee.21.2.11.

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License plate recognition is an essential part of contemporary surveillance systems since it is helpful in many applications, including parking management, vehicle access control, traffic control, and law enforcement. This project aims to provide a robust and dependable method for detecting license plates that will outperform existing approaches in accuracy and dependability. This observation method uses contemporary technology to address challenging troubles related to license plate recognition. Our methodology is primarily based on the Faster R-CNN structure, an established model for picture

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Kaur, Navjot, and Gurjeet Singh. "Detection and tracking of vehicles using license plate recognition." BOHR Journal of Computational Intelligence and Communication Network 1, no. 1 (2023): 1–6. http://dx.doi.org/10.54646/bjcicn.2023.01.

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The density of vehicles on roads has increased manifold over the last few decades. A seamless system to detect and track a particular vehicle can solve many problems, like traffic congestion, etc. This paper proposes the use of real-time data taken from closed-circuit televisions to detect and track the movements of vehicles. The feature extraction and classification are completely done by the process of faster RCNN (regional convolutional neural networks). The core work is based on region proposal networks. CNN furthers the generation of features; classification is done separately, but it is

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Kaur, Navjot, and Gurjeet Singh. "Detection and Tracking of Vehicles Using License Plate Recognition." BOHR International Journal of Computational Intelligence and Communication Network 2, no. 1 (2023): 1–6. http://dx.doi.org/10.54646/bijcicn.009.

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The density of vehicles on roads has increased manifold over the last few decades. A seamless system to detect and track a particular vehicle can solve many problems, like traffic congestion, etc. This paper proposes the use of real-time data taken from closed-circuit televisions to detect and track the movements of vehicles. The feature extraction and classification are completely done by the process of faster RCNN (regional convolutional neural networks). The core work is based on region proposal networks. CNN furthers the generation of features; classification is done separately, but it is

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Jose, John Anthony C., Allysa Kate M. Brillantes, Elmer P. Dadios, et al. "Recognition of Hybrid Graphic-Text License Plates." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 4 (2021): 416–22. http://dx.doi.org/10.20965/jaciii.2021.p0416.

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Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.

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Al-batat, Reda, Anastassia Angelopoulou, Smera Premkumar, Jude Hemanth, and Epameinondas Kapetanios. "An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification." Sensors 22, no. 23 (2022): 9477. http://dx.doi.org/10.3390/s22239477.

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An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization in complex real-life conditions. In this paper, we leverage a YOLO-based end-to-end generic ALPR pipeline for vehicle detection (VD), license plate (LP) detection and recognition without exploiting prior knowledge or additional steps in inference. We assess the whole ALPR pipeline,

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Salemdeeb, M., and S. Erturk. "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks." Engineering, Technology & Applied Science Research 10, no. 4 (2020): 5979–85. https://doi.org/10.5281/zenodo.4016176.

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Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multinational and multi-language detection, with either one-line

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Muhammad Ridwan Ali and Ario Yudo Husodo. "Pengenalan Plat Kendaraan Bermotor Menggunakan Metode Gradien Karakter dan BPNN (Backpropagation Neural Network)." Journal of Computer Science and Informatics Engineering (J-Cosine) 4, no. 2 (2020): 169–78. http://dx.doi.org/10.29303/jcosine.v4i2.328.

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License plates are a unique feature to identify a vehicle in the combination between letters and numbers. Feature extraction needed to identify each letter and number in a digital image. There are several methods in feature extraction, one of them uses a gradient feature extraction. In this research, an application program to identify the license plate is a character gradient method and backpropagation neural network (BNN). First, the digital image is cropped to get a license plate then segmented to generate each character. The next step is the extraction feature using Character gradient to ge

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Hakim, Heba, Zaineb Alhakeem, Hanadi Al-Musawi, Mohammed A. Al-Ibadi, and Alaa Al-Ibadi. "License Plate Detection and Recognition in Unconstrained Environment Using Deep Learning." Iraqi Journal for Electrical and Electronic Engineering 21, no. 1 (2025): 210–20. https://doi.org/10.37917/ijeee.21.1.21.

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Real-time detection and recognition systems for vehicle license plates present a significant design and implementation challenge, arising from factors such as low image resolution, data noise, and various weather and lighting conditions. This study presents an efficient automated system for the identification and classification of vehicle license plates, utilizing deep learning techniques. The system is specifically designed for Iraqi vehicle license plates, adapting to various backgrounds, different font sizes, and non-standard formats. The proposed system has been designed to be integrated i

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Ashok, Kumar Shrivastava. "A SFPM METHOD FOR INDIAN AUTOMOBILE RANGE PLATE RECOGNITION." INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH 5, no. 2 :SE (2018): 35–42. https://doi.org/10.5281/zenodo.1196752.

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Automobile range plate recognition is a challenging task in cyber crime. The numbers are stated of being in the automobile range plate, that is different shape and pattern in different countries. In India the automobile range plate uses white as background and black as foreground colour. In this paper we propose a SFPM methodology, first we find out the shape of license plate then enhance the image and calculate the characters of the license plate by using segmentations method. At the end of algorithm we apply fuzzy and pattern matching for character recognition. In our work we use two databas

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Borysov, Oleksii, and Olena Vasylieva. "Individual License Plates of Vehicles in the Communicative and Cognitive Perspective." Studia Philologica 2, no. 23 (2024): 20–32. https://doi.org/10.28925/2311-2425.2024.232.

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The article focuses on analyzing personalized license plates issued for vehicles of individuals and legal entities. A made-to-order plate is an official identification of a car or motorcycle on the roads of Ukraine. The article argues that these license plates demonstrate the owner’s linguistic creativity and represent a unique form of communication within the Ukrainian society. While standard license plates provided by the Ministry of Internal Affairs of Ukraine are impersonal and only serve to link a vehicle to its owner and a region of its registration, a personalized plate serves as a way

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Chou, Jui-Sheng, and Chia-Hsuan Liu. "Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning." Sensors 21, no. 2 (2021): 555. http://dx.doi.org/10.3390/s21020555.

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Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which ma

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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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Srividhya, S. R., C. Kavitha, Wen-Cheng Lai, Vinodhini Mani, and Osamah Ibrahim Khalaf. "A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection." Wireless Communications and Mobile Computing 2022 (June 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/9273233.

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In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video

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Ahmaron, Denar, Rudi Kurniwan, Yudhistira Arie Wijaya, and Rahmat Hidayat. "Prediction Model Optimization on Odd-Even License Plates Using YoloV8 Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 1712–19. https://doi.org/10.59934/jaiea.v4i3.997.

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Traffic congestion in urban areas encourages the implementation of vehicle restriction policies based on license plate numbers, such as the odd-even system. Therefore, to support this policy, an accurate vehicle license plate detection system is needed and can work in real-time. The main challenge faced is how to develop an accurate and efficient detection model in recognizing license plates in various environmental conditions. The research method used is Knowledge Discovery in Databases (KDD) with five main stages, namely: data selection, preprocessing, transformation, data mining, and evalua

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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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Ottakath, Najmath, and Somaya Al-Maadeed. "Vehicle Instance Segmentation Polygonal Dataset for a Private Surveillance System." Sensors 23, no. 7 (2023): 3642. http://dx.doi.org/10.3390/s23073642.

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Vehicle identification and re-identification is an essential tool for traffic surveillance. However, with cameras at every corner of the street, there is a requirement for private surveillance. Automated surveillance can be achieved through computer vision tasks such as segmentation of the vehicle, classification of the make and model of the vehicle and license plate detection. To achieve a unique representation of every vehicle on the road with just the region of interest extracted, instance segmentation is applied. With the frontal part of the vehicle segmented for privacy, the vehicle make

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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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Salemdeeb, M., and S. Erturk. "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks." Engineering, Technology & Applied Science Research 10, no. 4 (2020): 5979–85. http://dx.doi.org/10.48084/etasr.3573.

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Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multi-national and multi-language detection, with either one-line

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Omar, Naaman. "ResNet and LSTM Based Accurate Approach for License Plate Detection and Recognition." Traitement du Signal 39, no. 5 (2022): 1577–83. http://dx.doi.org/10.18280/ts.390514.

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The identification and recognition of automatic license plates (ALP) are critical for traffic surveillance, parking management, and preserving the rhythm of modern urban life. In this paper, a deep learning-based method is proposed for ALP. In the proposed work, the license plate region is initially segmented in a given vehicle image, and the plate number and city region are extracted from the segmented license plate region. Residual neural networks (ResNet) architecture-based deep feature extraction is considered. The fully connected layer of the ResNet model is used to obtain the deep featur

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Madde, Pavan Kumar, K. Manivel Dr., and Jayanthi N. "Classification and Detection of Vehicles using Deep Learning." International Journal of Trend in Scientific Research and Development 4, no. 3 (2020): 283–91. https://doi.org/10.5281/zenodo.3892595.

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The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. The traditional methods of vehicle classification and detection are highly complex which provides coarse grained results due to suffering from limited viewpoints. Because of the latest achievements of Deep Learning, it was successfully applied to image classification and detection of objects. This paper presents a method based on a convolutional neural network, which consists of two steps vehicle classification and vehicle license plate recognition. Several typical

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Krishnaveni, S. "Advancements in Safety: Utilizing CNNs for Helmet Detection and License Plate Recognition." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 25–30. http://dx.doi.org/10.22214/ijraset.2024.63519.

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Abstract: In contemporary times, road accidents stand out as significant contributors to human fatalities. Among these, motorcycle accidents are prevalent and often result in severe injuries. Helmets serve as crucial protective gear for motorcyclists, yet adherence to helmet laws remains lacking. To overcome this issue, a system that uses image processing and convolutional neural networks (CNNs) has been created. This system encompasses motorbike detection, helmet classification (helmet vs. no helmet), and motorbike license plate recognition. Motorbikes are initially identified using YOLOV3. A

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Muluk, Shivam, Ujjwal Mule, and Siddhesh Malode. "Number Plate Detection and Parking Allotment." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 6–10. http://dx.doi.org/10.22214/ijraset.2023.53546.

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Abstract: In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification.

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Jian, Rui, and Jun Zhao. "A Dynamic Sliding Window Based on Otsu Method for Binary License Plate and Character Recognition." Applied Mechanics and Materials 397-400 (September 2013): 2301–8. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2301.

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This paper is concerned with the problem of license plate recognition of vehicles. A recognition algorithm based on dynamic sliding window to binarize license plate characters is proposed. While a connected domain approach is presented to cope with the degradation characters. There are three steps to recognize the characters. First, the characters are classified by their features. Then, based on such classification a grid method is used to construct the feature vector. Finally, least square support vector machine is employed to recognize these characters. The test results show the high recogni

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Gao, Haofei, Wanrong Sun, Xinran Liu, and Ming Han. "Research and Implementation of a License Plate Recognition Algorithm Based on Hierarchical Classification." Journal of Computer and Communications 02, no. 02 (2014): 25–30. http://dx.doi.org/10.4236/jcc.2014.22005.

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Sharma R, Rajesh. "Classification of License Plate Images from Real-Time Vehicles Using Generative Adversarial Network." Bioscience Biotechnology Research Communications 14, no. 7 (2021): 223–28. http://dx.doi.org/10.21786/bbrc/14.7.51.

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王, 婧. "Discussion on the Illegal Acts and Its Classification Involving Motor Vehicle License Plate." Dispute Settlement 02, no. 04 (2016): 56–60. http://dx.doi.org/10.12677/ds.2016.24010.

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Mobile Computing, Wireless Communications and. "Retracted: A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection." Wireless Communications and Mobile Computing 2023 (November 1, 2023): 1. http://dx.doi.org/10.1155/2023/9856803.

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Swanand Joshi, Pramod Jejure, Chatrasal Jadhav, and Vishal Jankar. "Automatic Number Plate Recognition Using YOLOv8 Model." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 1088–97. https://doi.org/10.32628/ijsrst251222657.

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Automatic Number Plate Recognition (ANPR) systems have become a critical tool in various sectors, including traffic management, law enforcement, and tolling systems. This paper presents an in-depth exploration of an advanced ANPR framework that leverages cutting-edge image processing methodologies and machine learning models to deliver exceptional accuracy in license plate detection and recognition. The system follows a multi-phase approach encompassing image capture, preprocessing, plate localization, character segmentation, and optical character recognition (OCR). Notably, the integration of

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