Добірка наукової літератури з теми "Car license plate detection and recognition"
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Статті в журналах з теми "Car license plate detection and recognition"
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.
Повний текст джерелаMathew, Jess, and Chang Lee. "Vertical Edge Detection for Car License Plate Recognition." DJ Journal of Advances in Electronics and Communication Engineering 1, no. 1 (August 9, 2015): 8–15. http://dx.doi.org/10.18831/djece.org/2015011002.
Повний текст джерелаAlaidi, Abdul Hadi M., Saif Ali Abd Alradha Alsaidi, and Omar Hashim Yahya. "Plate Detection and Recognition of Iraqi License Plate Using KNN Algorithm." Journal of Education College Wasit University 1, no. 26 (January 12, 2017): 449–60. http://dx.doi.org/10.31185/eduj.vol1.iss26.102.
Повний текст джерелаHashmi, Saquib Nadeem, Kaushtubh Kumar, Siddhant Khandelwal, Dravit Lochan, and Sangeeta Mittal. "Real Time License Plate Recognition from Video Streams using Deep Learning." International Journal of Information Retrieval Research 9, no. 1 (January 2019): 65–87. http://dx.doi.org/10.4018/ijirr.2019010105.
Повний текст джерелаFarag, Mohamed Sayed, Mostafa Mohamed Mohie El Din, and Hassan Ahmed Elshenbary. "Parking entrance control using license plate detection and recognition." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 476. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp476-483.
Повний текст джерелаAldabbagh, Ali H. A., Laith A. H. Al-Shimaysawee, and Hussein M. H. Al-Rikabi. "Novel Algorithm for Iraqi Car License Plate Detection and Recognition." Journal of Engineering and Applied Sciences 14, no. 1 (December 10, 2019): 205–10. http://dx.doi.org/10.36478/jeasci.2019.205.210.
Повний текст джерелаAggarwal, Akarsh, Anuj Rani, and Manoj Kumar. "A robust method to authenticate car license plates using segmentation and ROI based approach." Smart and Sustainable Built Environment 9, no. 4 (October 4, 2019): 737–47. http://dx.doi.org/10.1108/sasbe-07-2019-0083.
Повний текст джерелаSvatiuk, Danylo, Oksana Svatiuk, and Oleksandr Belei. "APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM." Cybersecurity: Education, Science, Technique 4, no. 8 (2020): 97–112. http://dx.doi.org/10.28925/2663-4023.2020.8.97112.
Повний текст джерелаShevchenko, V., V. Bredikhin, T. Senchuk, and V. Verbytska. "COMPARISON OF METHODS FOR AUTOMATIC LICENSE NUMBER RECOGNITION." Municipal economy of cities 4, no. 171 (October 17, 2022): 7–11. http://dx.doi.org/10.33042/2522-1809-2022-4-171-7-11.
Повний текст джерелаManoj Prakash, P., Sreerag Premanathan, ShivamKumar Surwase, M. S. Antony Vigil, and Shivam Bohare. "License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3246–51. http://dx.doi.org/10.1166/jctn.2019.8171.
Повний текст джерелаДисертації з теми "Car license plate detection and recognition"
Ning, Guanghan. "Vehicle license plate detection and recognition." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157318.
Повний текст джерелаIn this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process.
Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%.
After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably.
The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features.
Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%.
The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulate the process of vehicle retrieval.
Colberg, Kathryn. "Investigating the ability of automated license plate recognition camera systems to measure travel times in work zones." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49048.
Повний текст джерелаKrajíček, Pavel. "Rozpoznání SPZ/RZ." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218307.
Повний текст джерелаD'amore, Luiz Angelo. "Robustez na segmentação de placas veiculares em condições complexas de aquisição." Universidade Presbiteriana Mackenzie, 2010. http://tede.mackenzie.br/jspui/handle/tede/1389.
Повний текст джерелаCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
The work presented here shows a robust method for license plate detection. The term robust in this work is directly related to the efficacy of the system as an automated locator of license plates without human intervention and considering specific characteristics of image acquisition and license plate features. The proposed method is based on the specify features of the digits found on the Brazilian license plates. Although the method was designed for the Brazilian license plate pattern it can be easily adjusted to other patterns. The results obtained using the proposed method showed a better performance than that of other academic approaches and even of commercial systems.
Os sistemas automáticos de reconhecimento de placas veiculares têm como principal função a identificação de veículos a partir de imagens digitais, com aplicações nas áreas de segurança pública e privada. Neste trabalho são apresentadas técnicas de processamento de imagens com o objetivo de desenvolver um método robusto para a segmentação de placas veiculares em condições complexas de aquisição. O termo robusto neste trabalho é relacionado diretamente à eficácia do sistema quanto à localização automática das placas veiculares sem intervenção humana, considerando características específicas das imagens e placas. O método proposto é baseado nas especificidades dos dígitos localizados nas placas brasileiras. Embora o método tenha sido projetado para o padrão de placas brasileiro, pode ser facilmente ajustado para outros padrões. Os resultados obtidos com o método proposto mostram um desempenho melhor que outras abordagens acadêmicas, ou mesmo de sistemas comerciais.
Vladimir, Tadić. "Fazifikacija Gaborovog filtra i njena primena u detekciji registarskih tablica." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=107171&source=NDLTD&language=en.
Повний текст джерелаThe thesis presents a new algorithm for detection and extraction of license plates from a vehicle image using a fuzzy two-dimensional Gabor filter. The filter parameters, orientation and wavelengths are fuzzified to optimize the Gabor filter’s response and achieve a greater selectivity. It was concluded that Bell’s function and triangular membership function are the most efficient methods for fuzzification. Algorithm was evaluated on several databases and has provided satisfactory results. The components of interest were efficiently extracted, and the procedure was found to be very noise-resistant.
Špaňhel, Jakub. "Re-identifikace vozidla pomocí rozpoznání jeho registrační značky." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-264932.
Повний текст джерелаLi, Hui. "Text detection and recognition in natural scene images." Thesis, 2018. http://hdl.handle.net/2440/115175.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018
tsai, Sung-nien, and 蔡松年. "Dynamic Car License Plate Detection." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/22083988530781627442.
Повний текст джерела中華技術學院
電子工程研究所碩士班
98
License plate recognition system is widely used in a lot of areas such as in the automation of parking lot toll station and in helping to detect stolen vehicle. Previous studies are essentially in static system needed to use a image in which the license plate is fangzheng, however, in this way the vehicle's location and the mobility and the environment must be satisfied some conditions. This study focuses on the dynamic vehicle license plate recognition using video car image when car is moving. In the first, the source images are filmed from the camera. Then, make a series of processes to the images as filtering, edge detection, binarization, rotation…etc. And then, using the binary images locates the preliminary position of the license plate. Finally, a template matching method is to be used to obtain more precise position of the license plate location. The actual recording films of the moving vehicle are used to test. The results of the test verify the effectiveness of the proposed method.
Chen, I.-Chih, and 陳奕志. "Constructing Embedded Car License Plate Recognition System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/91717196909075534200.
Повний текст джерела淡江大學
資訊工程學系碩士班
93
Embedded System is a computing platform designed for specific purpose. Because it’s task is much simpler than personal computer which is designed for general computing purpose, Embedded System can simplify its hardware architecture, cost down its hardware price, produce smaller device and low energy consumption. It also fits to be mobile computing platform. But Embedded System is constrained by its simple architecture; its processing power is much slower than personal computer. This paper purpose a software porting procedure between personal computer and Embedded System platform via a instance of Car License Plate Recognition System , and make Embedded Car License Plate Recognition System more efficient via exchange floating operation by integer operation and bitwise operation. For Example the image format captured by COMS, is Color Filter Array ,and this format will lost 2/3’s original image illumination. The illumination recovery process was originally involve with mass of floating operation, after applying the speeding method that we just mention before the processing time become times faster. We also use uClinux to assist hardware communication and process scheduling, and uClinux makes Embedded System able to handle complicated process control, also make software porting much smooth. Network File System ,NFS not only resolving the problem of lacking storage media, but also reduces the times of flash Rom burning procedure. Finally we use TCP/IP to transfer the image captured by CMOS to remote personal computer for Car License Plate Recognition ,and compare Car License Plate Recognition results between Embedded System and personal computer.
Wang, Ching-Chung, and 王精忠. "THE STUDY OF CAR LICENSE PLATE RECOGNITION SYSTEM." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/84668349117339820560.
Повний текст джерела大同大學
通訊工程研究所
93
Along with economical grow up and commerce activity vigorous development, people for the automobile need is more and more, although government for the traffic construction is very popular, but in the crowded Taiwan area, the question of parking space not enough is a fact of without saying, so how to manage parking lots efficiently and increasing usability of the parking lots that is our concerned question. This thesis proposed the license plate recognition system, includes license plate locating, image binarization, calibration of license plate, character segmentation, character recognition and so on, total five parts; In the license plate locating, we use the image process technique to process the input image of automobile change into fixed resolution gray image, use again Sobel edge detection method to find out the edge of license plate, at last use filter to find out the position of license plate; In the image binarization, we use dynamic threshold value method to find out threshold value, let gray image of license plate change into binarized image; In the calibration of license plate, we use bottom outline of license plate analysis method to find out slope angle of license plate and to execute calibration; In the character segmentation, we use vertical projection method to find out the high of character, and we use horizontal projection method to segment the characters of license plate, at last we use partial recognition method to recognize the number of license plate image. This system takes 200 license plate images from indoor and outdoor parking lots to execute the experiment of license plate recognition, experimental results, the license plate locating successful rate is 98%, the character segmentation successful rate is 95%, the character recognition successful rate is 93%, the average recognition time of each image needs 1.2 second.
Книги з теми "Car license plate detection and recognition"
Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.
Повний текст джерелаЧастини книг з теми "Car license plate detection and recognition"
Zhang, Wei, Yaobin Mao, and Yi Han. "SLPNet: Towards End-to-End Car License Plate Detection and Recognition Using Lightweight CNN." In Pattern Recognition and Computer Vision, 290–302. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60639-8_25.
Повний текст джерелаCaccia, Fabio, Roberto Marmo, and Luca Lombardi. "License Plate Detection and Character Recognition." In Image Analysis and Processing – ICIAP 2009, 471–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_51.
Повний текст джерелаRahman, Md J., S. S. Beauchemin, and M. A. Bauer. "License Plate Detection and Recognition: An Empirical Study." In Advances in Intelligent Systems and Computing, 339–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_24.
Повний текст джерелаNegri, Pablo, Mariano Tepper, Daniel Acevedo, Julio Jacobo, and Marta Mejail. "Multiple Clues for License Plate Detection and Recognition." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 269–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_38.
Повний текст джерелаIslam, Tariqul, and Dm Mehedi Hasan Abid. "Automatic Vehicle Bangla License Plate Detection and Recognition." In Algorithms for Intelligent Systems, 523–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3311-0_44.
Повний текст джерелаSharma, Vishal, Manvi Jain, Tanvi Jain, and Rashmi Mishra. "License Plate Detection and Recognition Using OpenCV–Python." In Lecture Notes in Electrical Engineering, 251–61. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8248-3_20.
Повний текст джерелаSilva, Sérgio Montazzolli, and Cláudio Rosito Jung. "License Plate Detection and Recognition in Unconstrained Scenarios." In Computer Vision – ECCV 2018, 593–609. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_36.
Повний текст джерелаSoghadi, Zahra Taleb, and Ching Y. Suen. "License Plate Detection and Recognition by Convolutional Neural Networks." In Pattern Recognition and Artificial Intelligence, 380–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_33.
Повний текст джерелаLi, Zhen-Jia, Song-Lu Chen, Qi Liu, Feng Chen, and Xu-Cheng Yin. "Anchor-Free Location Refinement Network for Small License Plate Detection." In Pattern Recognition and Computer Vision, 506–19. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18916-6_41.
Повний текст джерелаVig, Simar, Archita Arora, and Greeshma Arya. "Automated License Plate Detection and Recognition Using Deep Learning." In Advancements in Interdisciplinary Research, 419–31. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23724-9_39.
Повний текст джерелаТези доповідей конференцій з теми "Car license plate detection and recognition"
Ahn, Chi-Sung, Bong-Gyou Lee, Seung-Su Yang, and Seok-Cheon Park. "Design of car license plate area detection algorithm for enhanced recognition plate." In 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT). IEEE, 2017. http://dx.doi.org/10.1109/caipt.2017.8320749.
Повний текст джерелаLi, Wei-Chen, Ting-Hsuan Hsu, Ke-Nung Huang, and Chou-Chen Wang. "A YOLO-Based Method for Oblique Car License Plate Detection and Recognition." In 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2021. http://dx.doi.org/10.1109/snpd51163.2021.9704935.
Повний текст джерелаHan, Mengcheng, and Yu Sun. "An Intelligent Mobile Application for Car License Plate Detection and Analysis using Machine Learning Algorithm." In 9th International Conference on Artificial Intelligence and Applications (AIAP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120406.
Повний текст джерелаTom, Ruben Jose, Awanit Kumar, Syed Basha Shaik, Lydia D. Isaac, Vikas Tripathi, and Prakash Pareek. "Car License Plate Detection and Recognition Using Modified U-Net Deep Learning Model." In 2022 8th International Conference on Smart Structures and Systems (ICSSS). IEEE, 2022. http://dx.doi.org/10.1109/icsss54381.2022.9782176.
Повний текст джерелаPinto, Pedro Ferreira Alves, Antonio José G. Busson, João P. Forny de Melo, Sérgio Colcher, and Ruy Luiz Milidiú. "PVBR-Recog: A YOLOv3-based Brazilian Automatic License Plate Recognition Tool." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8149.
Повний текст джерелаZhong, Yuwen, Yonggui Liu, Fei Luo, and Hang Zhang. "A Novel Integrated Neural Network for License Plate Detection And Recognition." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9326612.
Повний текст джерелаSaadouli, Ghaida, Maha Ibrahim Elburdani, Razan Mohammed Al-Qatouni, Suchithra Kunhoth, and Somaya Al-Maadeed. "Automatic and Secure Electronic Gate System Using Fusion of License Plate, Car Make Recognition and Face Detection." In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). IEEE, 2020. http://dx.doi.org/10.1109/iciot48696.2020.9089615.
Повний текст джерелаOmran, Safaa, and Jumana Jarallah. "Iraqi Car License Plate Recognition Using OCR." In 2nd International Conference of Cihan University-Erbil on Communication Engineering and Computer Science. Cihan University-Erbil, 2017. http://dx.doi.org/10.24086/cocos17.19.
Повний текст джерелаOmran, Safaa S., and Jumana A. Jarallah. "Iraqi car license plate recognition using OCR." In 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT). IEEE, 2017. http://dx.doi.org/10.1109/ntict.2017.7976127.
Повний текст джерелаChai, Hum Yan, Hon Hock Woon, Liang Kim Meng, and Yuen Shang Li. "Non-standard Malaysian car license plate recognition." In 2014 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2014. http://dx.doi.org/10.1109/iscaie.2014.7010228.
Повний текст джерела