Relevant bibliographies by topics / License Plate Recognition in Android

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Academic literature on the topic 'License Plate Recognition in Android'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "License Plate Recognition in Android"

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Madhan, S., and M. Pradeep. "IMAGE PROCESSING OF ANDROID-BASED PATROL ROBOT FEATURING AUTOMATIC LICENSE PLATE RECOGNITION." International Journal of Students' Research in Technology & Management 3, no. 3 (2015): 296–301. http://dx.doi.org/10.18510/ijsrtm.2015.336.

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This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad
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Sun, Yueyue, and Xuechen Zhao. "Research and implementation of license plate recognition based on android platform." MATEC Web of Conferences 309 (2020): 03034. http://dx.doi.org/10.1051/matecconf/202030903034.

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This paper studies and optimizes license plate location and recognition in license plate recognition. A license plate recognition system based on Android platform is designed and implemented. Opencv and Tesseract OCR are integrated in Android studio environment. The license plate number is located by combining Laplace algorithm and HSV model. On the basis of fully understanding the principle of Tesseract OCR recognition, a large number of training pictures are generated by license plate number simulation generator, and license plate character library is generated by using jtessboxeditor tool,
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Svatiuk, 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.

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The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To s
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Katunda, Ramadhani. "Portable License Plate Recognition System on Android devices: Case study Law enforcement in Tanzania." Joho Chishiki Gakkaishi 28, no. 2 (2018): 155–60. http://dx.doi.org/10.2964/jsik_2018_014.

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Lee, Hee-Yeol, and Seung-Ho Lee. "A Study On Low-cost LPR(License Plate Recognition) System Based On Smart Cam System using Android." Journal of IKEEE 18, no. 4 (2014): 471–77. http://dx.doi.org/10.7471/ikeee.2014.18.4.471.

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Poltavskii, A. V., T. G. Yurushkina, and M. V. Yurushkin. "Automatic license-plate recognition." Vestnik of Don State Technical University 20, no. 1 (2020): 93–99. http://dx.doi.org/10.23947/1992-5980-2020-20-1-93-99.

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Chang, S. L., L. S. Chen, Y. C. Chung, and S. W. Chen. "Automatic License Plate Recognition." IEEE Transactions on Intelligent Transportation Systems 5, no. 1 (2004): 42–53. http://dx.doi.org/10.1109/tits.2004.825086.

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

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Extraction of License plate region is an important stage in the intelligent vehicle license plate recognition system. A practical license plate extraction algorithm based on edge detection and mathematical morphology is presented, the algorithm mainly consists of six modules: pre-processing, edge detection, binaryzation and denoising, morphology operation, filtration of connected regions, finding license plate region. From the experiments, the algorithm can detect the region of license plate quickly with 98% average accuracy of locating vehicle license plate region.
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Findley, Daniel J., Christopher M. Cunningham, Jeffrey C. Chang, Kyle A. Hovey, and Michael A. Corwin. "Effects of License Plate Attributes on Automatic License Plate Recognition." Transportation Research Record: Journal of the Transportation Research Board 2327, no. 1 (2013): 34–44. http://dx.doi.org/10.3141/2327-05.

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

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

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

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License plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring, expressway toll system, electronic-police system, and security systems. In recent years, there has been a lot of research in license plate recognition, and many recognition systems have been proposed and used. But these systems have been developed for computers. In this project, we developed a mobile LPR system for Android Operating System (OS). LPR involves three main components: license plate detection, character segmentation a
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Hortai, František. "Zpracování obrazu v systému Android - detekce a rozpoznání SPZ/RZ." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220899.

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This thesis describes the design and workflow of creating an image processing application in Android system, and what are the possibilities in choosing development environment and how to implement them. Then I am writing about my solutions of creating applications, graphical user interface and an interface for Android. I am describing my approach in the design and functionality of the application, communication with the camera, storing and retrieving data. Further I explain which algorithms were implemented for image processing and image evaluation. Product of this thesis is a functioning appl
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Songke, Li, and Chen Yixian. "License plate recognition." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9442.

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

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

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

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

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License Plate Recognition (LPR) systems are frequently utilized in various access controls and security applications. In this thesis, an experimental constraint based real-time License Plate Recognition system is designed, and implemented in Java platform. Many of the available constraint based methods worked under strict restrictions such as plate color, fixed illumination and designated routes, whereas, only the license plate geometry and format constraints are used in this developed system. These constraints are built on top of the current Turkish license plate regulations. The plate locali
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Chen, Kai-Min, and 陳楷旻. "Shadow Removal Method for License Plate Recognition and Its Android Electronic License Plate Recognition System Implementation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/96096944989924891612.

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碩士<br>國立雲林科技大學<br>電機工程系碩士班<br>101<br>In License Plate Recognition (LPR) system, License Plate Detection (LPD) and binarization are both key image processing steps. But, under indoor or outdoor scene environments, LPD and binarization are both error-prone to the shadow interference caused by the shadow occlusion on the license plate scene images. Especially for diverse applications of potable and fixed LPR systems, shadow occlusion and interference issue resulting from uneven lighting condition is always necessary to be overcome. So this thesis proposes two shadow removal methods to solve the s
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Chiang, Chang-Yun, and 江長運. "Plate- and Character-based AdaBoost License Plate Detection and Its Android License Plate Recognition Recorder Implementation." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/86923239518107813208.

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碩士<br>國立雲林科技大學<br>電機工程系碩士班<br>100<br>License Plate Detection (LPD) is the fundamental step to License Plate Recognition (LPR). If the license plate localization result of LPD is not exact enough, subsequent OCR step is unnecessary to be carried out. Most especially, on mobile or portable LPR devices, the accuracy of LPD severely suffers from the environmental interferences, like complex background, illumination variation, scene alternation. So most traditional LPR system only can work out for invariant and specific scenes. In order to overcome the LPD difficulty of mobile or portable LPR devic
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Tsao, Jung-Chieh, and 曹榮傑. "A Synthesis of License Plate Recognition System Based on Android Platform." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/24867380101030075849.

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碩士<br>國立高雄應用科技大學<br>電機工程系博碩士班<br>103<br>In the past, intelligent transportation technology is mainly used in the management of the parking lot and clamp down on illegal vehicles, electronic toll collection system and so on, but with the development of the Internet of Things quickly, the rise of smartphones and increasing number of vehicles, intelligent transportation technology has new thought, such as using mobile robot to license plate detection. The traditional license plate recognition system can’t satisfy the requirements which can carry, so the article provides the license plate recogn
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Books on the topic "License Plate Recognition in Android"

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

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

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

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

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

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

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

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Zhuang, Jiafan, Saihui Hou, Zilei Wang, and Zheng-Jun Zha. "Towards Human-Level License Plate Recognition." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01219-9_19.

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

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

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

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Cano, Javier, and Juan-Carlos Pérez-Cortés. "Vehicle License Plate Segmentation in Natural Images." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_17.

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Conference papers on the topic "License Plate Recognition in Android"

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Chen, Hsin-Fu, Chang-Yun Chiang, Shih-Jui Yang, and Chian C. Ho. "Android-based patrol robot featuring automatic license plate recognition." In 2012 Computing, Communications and Applications Conference (ComComAp). IEEE, 2012. http://dx.doi.org/10.1109/comcomap.2012.6154014.

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Astawa, I. Nyoman Gede Arya, I. Gusti Ngurah Bagus Caturbawa, Elina Rudiastari, Made Leo Radhitya, and Ni Kadek Dessy Hariyanti. "Convolutional Neural Network Method Implementation for License Plate Recognition in Android." In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT). IEEE, 2018. http://dx.doi.org/10.1109/eiconcit.2018.8878658.

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Chuin-Mu Wang and Ching-Yuan Su. "Fast license plate location and recognition using wavelet transform in android." In 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2012. http://dx.doi.org/10.1109/iciea.2012.6360875.

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Chuin-Mu Wang and Jian-Hong Liu. "License plate recognition system." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382203.

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

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Hui Wu and Bing Li. "License plate recognition system." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6003138.

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Khan, Nadeem A. M., Ron J. De la Haye, and Hans A. Hegt. "License plate recognition system." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Andrew G. Tescher. SPIE, 1998. http://dx.doi.org/10.1117/12.323154.

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

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Megalingam, Rajesh Kannan, Prasanth Krishna, Pratheesh somarajan, Vishnu A. Pillai, and Reswan Ul Hakkim. "Extraction of license plate region in Automatic License Plate Recognition." In 2010 2nd International Conference on Mechanical and Electrical Technology (ICMET). IEEE, 2010. http://dx.doi.org/10.1109/icmet.2010.5598409.

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Amirgaliyev, B. Y., C. A. Kenshimov, K. K. Kuatov, M. Z. Kairanbay, Z. Y. Baibatyr, and A. K. Jantassov. "License plate verification method for automatic license plate recognition systems." In 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO). IEEE, 2015. http://dx.doi.org/10.1109/icecco.2015.7416892.

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Reports on the topic "License Plate Recognition in Android"

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

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

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