Relevant bibliographies by topics / Automobile licence plates

Contents

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

Academic literature on the topic 'Automobile licence plates'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Automobile licence plates"

1

Ahn, Young-Joon, Kyu-Bum Wee, and Man-Pyo Hong. "Recognition of Chinese Automobile License Plates." KIPS Transactions:PartB 14B, no. 2 (April 30, 2007): 81–88. http://dx.doi.org/10.3745/kipstb.2007.14-b.2.081.

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Durand, Elaine S. "A Teacher's Journal: Automobile License Plates." Teaching Children Mathematics 4, no. 4 (December 1997): 206–8. http://dx.doi.org/10.5951/tcm.4.4.0206.

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Students develop mathematical power when they are able to acknowledge, use, and assess the mathematics they encounter in their daily lives. An awareness of mathematics in the world in which one lives provides opportunities for building confidence, enhancing self-esteem, and stimulating creativity.
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Luft, Edward R. "Use of Automobile License Plates to Teach the Concept of Place." Journal of Geography 91, no. 2 (March 1992): 62–66. http://dx.doi.org/10.1080/00221349208979080.

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Adedayo, Kayode David, and Ayomide Oluwaseyi Agunloye. "Real-time Automated Detection and Recognition of Nigerian License Plates via Deep Learning Single Shot Detection and Optical Character Recognition." Computer and Information Science 14, no. 4 (August 24, 2021): 11. http://dx.doi.org/10.5539/cis.v14n4p11.

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License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.
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Chung, Youchung, and Teklebrhan H. Berhe. "Long-Range UHF RFID Tag for Automotive License Plate." Sensors 21, no. 7 (April 4, 2021): 2521. http://dx.doi.org/10.3390/s21072521.

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In this paper, various locations of an Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tag on automotive license plates have been considered based on the radiation pattern of the tag antenna. A small, 130 × 50 mm, passive loop antenna type UHF RFID tag for an automotive license plate was simulated with an EM simulation CST program. It was designed to have a larger back-lobe radiation pattern since the front side of the tag faces the back side of the plate holder to protect the tag antenna from bugs and dust when the automobile runs. The tag was attached to the side of a license plate holder with a dimension of 520 × 110 mm, the typical size of the standard license plate. The reflection coefficient of the tag antenna is −21 dB at 920 MHz, and the gain of the tag antenna is 6.29 dBi at the back-lobe. The reading range of the tag antenna with the plate holder, which was measured in an open field, is about 10.3 m, and the reading range of the tag installed on the bumper from the front of the vehicle is 9.4 m. The tag antenna is small enough to apply to a real automobile, and it is applicable because it uses the back-lobe pattern, so it does not require an extra device for protection from damage.
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Ghubaish, Ali, Tara Salman, and Raj Jain. "Experiments with a LoRaWAN-Based Remote ID System for Locating Unmanned Aerial Vehicles (UAVs)." Wireless Communications and Mobile Computing 2019 (October 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/9060121.

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Federal Aviation Administration (FAA) of the United States is considering Remote ID systems for unmanned aerial vehicles (UAVs). These systems act as license plates used on automobiles, but they transmit information using radio waves. To be useful, the transmissions in such systems need to reach long distances to minimize the number of ground stations to capture these transmissions. LoRaWAN is designed as a cheap long-range technology to be used for long-range communication for the Internet of Things. Several manufacturers make LoRaWAN modules, which are readily available on the market and are, therefore, ideal for the UAVs Remote IDs at a low cost. In this paper, we present our experiences in using LoRaWAN technology as a communication technology. Our experiments to identify and locate the UAV systems uncovered several issues of using LoRaWAN in such systems that are documented in this paper. Using several ground stations, we can determine the location of a UAV equipped with a LoRaWAN module that transmits the UAV Remote ID. Hence, it can help identify UAVs that unintentionally, or intentionally, fly into restricted zones.
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"Soft Computing Techniques Based Automatic Licence Plate Recognition Systems for Indian Vehicles." International Journal of Innovative Technology and Exploring Engineering 8, no. 12 (October 10, 2019): 470–74. http://dx.doi.org/10.35940/ijitee.l3344.1081219.

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Automobile industries are growing exponentially in last decade in India. Growth in the vehicle numbers results in much more road accidents and traffic management problem. Not only this, long queues at toll plazas and parking lot is also a major issue of concern. Problem of traffic management and long queues can be solved by automatic licence plate recognition systems. In this paper, an automatic Licence Plate Recognition Systems based on soft computing techniques are presented. Indian vehicle with licence plates were used for testing the implemented systems. Firstly the licence plate image is extracted from the vehicle image and the characters are segmented from the extracted licence plate image and then features are extracted from the segmented characters which are used for the recognition. Soft computing techniques random forest, neural network, support vector machine, and convolutional neural network are used for the implementation pusrpose. The results obtained for the applied soft computing technique are compared to the last. The future scope is the hybrid technique solution to the problem
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Dissertations / Theses on the topic "Automobile licence plates"

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Van, Heerden Renier Pelser. "Hidden Markov models for robust recognition of vehicle licence plates." Diss., University of Pretoria, 2002. http://hdl.handle.net/2263/29402.

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In this dissertation the problem of recognising vehicle licence plates of which the sym¬bols can not be segmented by standard image processing techniques is addressed. Most licence plate recognition systems proposed in the literature do not compensate for dis¬torted, obscured and damaged licence plates. We implemented a novel system which uses a neural network/ hidden Markov model hybrid for licence plate recognition. We implemented a region growing algorithm, which was shown to work well when used to extract the licence plate from a vehicle image. Our vertical edges algorithm was not as successful. We also used the region growing algorithm to separate the symbols in the licence plate. Where the region growing algorithm failed, possible symbol borders were identified by calculating local minima of a vertical projection of the region. A multilayer perceptron neural network was used to estimate symbol probabilities of all the possible symbols in the region. The licence plate symbols were the inputs of the neural network, and were scaled to a constant size. We found that 7 x 12 gave the best character recognition rate. Out of 2117 licence plate symbols we achieved a symbol recognition rate of 99.53%. By using the vertical projection of a licence plate image, we were able to separate the licence plate symbols out of images for which the region growing algorithm failed. Legal licence plate sequences were used to construct a hidden Markov model contain¬ing all allowed symbol orderings. By adapting the Viterbi algorithm with sequencing constraints, the most likely licence plate symbol sequences were calculated, along with a confidence measure. The confidence measure enabled us to use more than one licence plate and symbol segmentation technique. Our recognition rate increased dramatically when we com¬bined the different techniques. The results obtained showed that the system developed worked well, and achieved a licence plate recognition rate of 93.7%.
Dissertation (MEng (Computer Engineering))--University of Pretoria, 2006.
Electrical, Electronic and Computer Engineering
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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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Dennis, Frank R. "The Effect of Color on Character Recognition: A Study of the Oregon License Plate." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4576.

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This study investigated character color changes on the current Oregon license plate in an attempt to rectify character recognition problems currently experienced with personalized license plates. Red-violet, brown, and purple were tested against the blue color currently used on the Oregon license plate's characters. Aesthetic-preference analyses were conducted to ensure that the potential character color changes maintained the appeal of the current plate. A standard recognition paradigm was used to test errors in letter recognition for the 4 colors. Fifty-four subjects with normal or corrected normal visual acuity and normal color vision were solicited from undergraduate psychology courses. All 54 volunteers participated in the first experiment which scaled preferences of the four character color alternatives using Thurstonian scaling. Fifty subjects were used in the second experiment which examined character recognition differences. The analyses focused on the center character of a 3- character string positioned with the central character on the green tree. Thurstonian scaling results indicated that the current blue character color was most preferred and brown was least preferred. ANOVA results found significant differences in character recognition between the four colors. The current blue color yielded the best character recognition, followed by red violet, purple and then brown. The findings were not congruent with Indow's (1988) study suggesting colors further removed from green on the color cognitive map should produce superior character recognition. A theoretical explanation of the results indicating that brightness differences, not hue, may have led to blue's superior performance is discussed.
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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.

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This thesis evaluates the performance of a vehicle detection technology, Automated License Plate Recognition (ALPR) camera systems, with regards to its ability to produce real-time travel time information in active work zones. A literature review was conducted to investigate the ALPR technology as well as to identify other research that has been conducted using ALPR systems to collect travel time information. Next, the ALPR technology was tested in a series of field deployments in both an arterial and a freeway environment. The goal of the arterial field deployment was to evaluate the optimal ALPR camera angles that produce the highest license plate detection rates and accuracy percentages. Next, a series of freeway deployments were conducted on corridors of I-285 in Atlanta, Georgia in order to evaluate the ALPR system in active work zone environments. During the series of I-285 freeway deployments, ALPR data was collected in conjunction with data from Bluetooth and radar technologies, as well as from high definition video cameras. The data collected during the I-285 deployments was analyzed to determine the ALPR vehicle detection rates. Additionally, a script was written to match the ALPR reads across two data collection stations to determine the ALPR travel times through the corridors. The ALPR travel time data was compared with the travel time data produced by the Bluetooth and video cameras with a particular focus on identifying travel time biases associated with each given technology. Finally, based on the knowledge gained, recommendations for larger-scale ALPR work zone deployments as well as suggestions for future research are provided.
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Bagušinskas, Donatas. "Automobilio numerio identifikavimo algoritmai." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110615_135256-26171.

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Šiame darbe aprašytas automobilio numerio identifikavimo uždavinys, šio uždavinio skaidymas į smulkesnes užduotis, tokias kaip numerio lokalizavimas nuotraukoje, numerio suskaidymas į atskirus simbolius, bei rastų simbolių identifikavimas. Kiekvienai šiai užduočiai realizuota po du algoritmus. Numerio lokalizavimo algoritmai remiasi kontūrų išskyrimu ir spalvų intensyvumu, bei morfologinėmis operacijomis nuotraukoje. Surasto numerio skaidymo į simbolius algoritmai remiasi statistiniu pasiskirstymu bei objektų išskyrimu. Simboliams atpažinti panaudota koreliacijos skaičiavimas tarp šablonų ir atpažįstamojo simbolio, bei dirbtinis neuroninis tinklas. Taip pat apžvelgta šiuo metu pasaulyje naudojamas technologijas ir algoritmai, sprendžiantys šį uždavinį bei palyginti rezultatai su savais.
This paper describes the task of vehicle number identification. It is further divided into smaller tasks, such as the localization of the number in the picture, the splitting of the number into individual symbols and the identification of the located symbols. Two algorithms were allocated for each task. The number localization algorithm is based on the exclusion of the contours, colour intensity and morphological operations in the picture. The algorithms used to divide the symbols of the identified number are based on the statistical distribution and object separation. Correlation calculation is used between the templates, identifiable symbol and artificial neural network. Moreover, the paper discusses the technologies and algorithms currently used across the world when dealing with such task, and compares it with the results of this paper.
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Chen, Songqing. "Development of a neural network based software package for the automatic recognition of license plate characters." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1172092299.

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Parthasarathy, Gayathri. "Automated license plate recognition a novel approach using spectral analysis and majority vote neural networks /." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433377.

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Girjotas, Andrius. "Transporto priemonių numerių atpažinimo algoritmų analizė bei universalios atpažinimo sistemos teorija." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20140702_193526-52512.

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Automatinis transporto priemonių registracijos numerio atpažinimas atlieka svarbų vaidmenį daugelyje programinių paketų, taikomų tiek valstybinėse institucijose, tiek ir privačiose kompanijose, kuriuose yra naudojamos įvairios atpažinimo algoritmų technologijos. Tačiau net ir dabar neįmanoma sukurti idealiai veikiančios sistemos, kuri palieka laisvę efektyviausių algoritmų paieškai. Šio tiriamojo darbo tikslas yra išanalizuoti alternatyvius automobilio numerio lokalizacijos ir kitų atpažinimo etapų algoritmus, jų efektyvumą bei adaptyvumą. Analizė atliekama juos realizuojant ir atliekant tyrimus su testiniais duomenimis bei iš jų gautais rezultatais. Iš realizuotos alternatyvių atpažinimo algoritmų sistemos gauti rezultatai parodė, kad kiekviena atpažinimo proceso grandis yra jautri įvairiems faktoriams, kurių kitimas lemia tarpinių bei galutinių rezultatų variaciją.
Automatic license plate recognition plays an important role in numerous applications and a number of techniques have been proposed for public institutions or private companies. However, even now it is impossible to design a perfect and operational recognition system. It still leaves a space for creativity and research of the most effective algorithms. The main objective of this dissertation is to analyze alternatives of licese plate localization and other stages of recognition, their efficiency and adaptability. Selected means of this research are such as implementation of algorithms, analysis of testing data and test results. Every stage of recognition process is extremely sensitive to different factors which determinate variation of transitional and final results. This was proven by analysis of alternative algorithms functionality.
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Kazlauskas, Tomas. "Transporto priemonių numerių atpažinimo algoritmų analizė bei universalios atpažinimo sistemos teorija." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20140702_193534-66751.

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Automatinis transporto priemonių registracijos numerio atpažinimas atlieka svarbų vaidmenį daugelyje programinių paketų, taikomų tiek valstybinėse institucijose, tiek ir privačiose kompanijose, kuriuose yra naudojamos įvairios atpažinimo algoritmų technologijos. Tačiau net ir dabar neįmanoma sukurti idealiai veikiančios sistemos, kuri palieka laisvę efektyviausių algoritmų paieškai. Šio tiriamojo darbo tikslas yra išanalizuoti alternatyvius automobilio numerio lokalizacijos ir kitų atpažinimo etapų algoritmus, jų efektyvumą bei adaptyvumą. Analizė atliekama juos realizuojant ir atliekant tyrimus su testiniais duomenimis bei iš jų gautais rezultatais. Iš realizuotos alternatyvių atpažinimo algoritmų sistemos gauti rezultatai parodė, kad kiekviena atpažinimo proceso grandis yra jautri įvairiems faktoriams, kurių kitimas lemia tarpinių bei galutinių rezultatų variaciją.
Automatic license plate recognition plays an important role in numerous applications and a number of techniques have been proposed for public institutions or private companies. However, even now it is impossible to design a perfect and operational recognition system. It still leaves a space for creativity and research of the most effective algorithms. The main objective of this dissertation is to analyze alternatives of licese plate localization and other stages of recognition, their efficiency and adaptability. Selected means of this research are such as implementation of algorithms, analysis of testing data and test results. Every stage of recognition process is extremely sensitive to different factors witch determinate variation of transitional and final results. This was proven by analysis of alternative algorithms functionality.
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Boztoprak, Halime Merdan Mustafa. "Gerçek zamanlı taşıt plaka tanıma sistemi /." Isparta : SDÜ Fen Bilimleri Enstitüsü, 2007. http://tez.sdu.edu.tr/Tezler/TF01139.pdf.

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Books on the topic "Automobile licence plates"

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Illinois. Office of Secretary of State. Collegiate license plates. Springfield, Ill.]: [Illinois Office of] Secretary of State, 2001.

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Illinois. Office of Secretary of State. Collegiate license plates. Springfield, Ill.]: [Illinois Office of] Secretary of State, 2003.

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Illinois. Office of Secretary of State. Collegiate license plates. Springfield, Ill.]: [Illinois Office of] Secretary of State, 2002.

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State, Illinois Office of Secretary of. Collegiate license plates. 2nd ed. Springfield, Ill.]: [Illinois Office of] Secretary of State, 2000.

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State, Illinois Office of Secretary of. Prevent violence license plates. Springfield, Ill.]: [Illinois Office of Secretary of State], 2003.

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Illinois. Office of Secretary of State. Collegiate license plates, 1996. Springfield, IL: Illinois Secretary of State, 1996.

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Plates, Maine Task Force on Production and Issuance of License. License plate design and production: Report to the first session of the One Hundred and Eighteenth Legislature's Transportation Committee. [Augusta, Me.?]: The Task Force, 1996.

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Illinois. Office of Secretary of State. Vehicle titles & license plates for new Illinois residents. Springfield, Ill]: Jesse White, Secretary of State [Vehicle Services Dept.], 2009.

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Illinois. Vehicle Services Dept. Mammogram license plates. Springfield, Ill.]: Jesse White, Secretary of State [Vehicle Services Dept., 2007.

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Illinois. Vehicle Services Department. Mammogram license plates. Springfield, Ill.]: Jesse White, Secretary of State [Vehicle Services Dept.], 2003.

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Book chapters on the topic "Automobile licence plates"

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Powe, Lucas A. "Freedom of Speech and the Press." In America's Lone Star Constitution. University of California Press, 2018. http://dx.doi.org/10.1525/california/9780520297807.003.0009.

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This chapter examines Supreme Court cases that were filed over the issue of freedom of speech and the press in Texas. In 1943, the Texas legislature passed the anti-labor Manford Act, which required labor unions and union agents to register and file comprehensive annual reports, while also forbidding them from making political contributions. The Manford Act was immediately put to the test by R. J. Thomas, president of the United Automobile, Aircraft, and Agricultural Implements Union and vice president of the Congress of Industrial Organizations. The chapter first discusses this case before analyzing other cases that followed, including those involving John Stanford, Louis C. Acker, Ray Hill, and Billie Sol Estes. It also considers Allee v. Medrano, Texas v. Johnson, and the issue of license plates in Walker v. Texas Division, Sons of Confederate Veterans.
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Conference papers on the topic "Automobile licence plates"

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Rezaei, Hossein, Maryam Haghshenas, and Mahboobehsadat Yasini. "Recognizing Persian Automobile license plates under adverse rainy conditions." In 2020 International Conference on Machine Vision and Image Processing (MVIP). IEEE, 2020. http://dx.doi.org/10.1109/mvip49855.2020.9116886.

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Mitchell, Maury, Matthe Hudnall, David Brown, David Cordes, Randy Smith, and Allen Parrish. "A Host Architecture for Automobile License Plate Recognition." In 2007 IEEE Intelligence and Security Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isi.2007.379539.

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Trapeznikov, Ilya, Andrey Priorov, and Vladimir Volokhov. "Allocation of text characters of automobile license plates on the digital image." In 2014 15th Conference of Open Innovations Association FRUCT and 3rd Regional Seminar on e-Tourism (FRUCT). IEEE, 2014. http://dx.doi.org/10.1109/fruct.2014.6872421.

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Al-qudah, Rabiah, and Ching Y. Suen. "Impact of Font on Computer Recognition of License Plates on Automobiles." In ICVISP 2019: 3rd International Conference on Vision, Image and Signal Processing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3387168.3389112.

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Qiu Chengqun. "Design of Automobile License Plate Recognition System Based on MATLAB and Fuzzy PID." In 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2013). IEEE, 2013. http://dx.doi.org/10.1109/icmtma.2013.110.

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Chang, Joongho, and Norman C. Griswold. "Hierarchical multilayer perceptron neural network for the recognition of the automobile license plate number." In Electronic Imaging: Science & Technology, edited by Nasser M. Nasrabadi and Aggelos K. Katsaggelos. SPIE, 1996. http://dx.doi.org/10.1117/12.234249.

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Bhutta, M. Usman Maqbool, Hasan Mahmood, and Hafiz Malik. "An intelligent approach for robust detection and recognition of multiple color and font styles automobiles license plates: A feature-based algorithm." In 2014 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2014. http://dx.doi.org/10.1109/icalip.2014.7009936.

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