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

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Li, Bo, Zhi Yuan Zeng, Hua Li Dong, and Xiao Ming Zeng. "Automatic License Plate Recognition System." Applied Mechanics and Materials 20-23 (January 2010): 438–44. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.438.

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This paper proposed an algorithm for license plate recognition system(LPRS). The vertical edge was first detected by sobel color edge detector. Then, the invalid edge was removed regarding edge density. Next, the license plate(LP) image was converted into HSV color model, and by edge density template and fuzzy color information judgement, the LP region was located. Then, color-reversing judgement and tilt correction was conducted. Afterward, characters were segmented by means of vertical projection and convolution, by which character width and position can be exactly confirmed, and character r

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12

Ktata, Sami, Taher Khadhraoui, Faouzi Benzarti, and Hamid Amiri. "Tunisian License Plate Number Recognition." Procedia Computer Science 73 (2015): 312–19. http://dx.doi.org/10.1016/j.procs.2015.12.038.

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13

Hsu, Gee-Sern, Jiun-Chang Chen, and Yu-Zu Chung. "Application-Oriented License Plate Recognition." IEEE Transactions on Vehicular Technology 62, no. 2 (2013): 552–61. http://dx.doi.org/10.1109/tvt.2012.2226218.

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14

Zhao, Youting, Zhi Yu, Xiying Li, and Ming Cai. "Chinese license plate image database building methodology for license plate recognition." Journal of Electronic Imaging 28, no. 01 (2019): 1. http://dx.doi.org/10.1117/1.jei.28.1.013001.

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15

Alaidi, 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 (2017): 449–60. http://dx.doi.org/10.31185/eduj.vol1.iss26.102.

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This paper presents an automated car license plate recognition system applied for Iraqi vehicle plate number that developed and applied to be used in control and law enforcement related applications. In this work, the proposed license plate recognition consists of three basic stages (preprocessing, license plate localization, license plate recognition). The license plate images are pre-processed through convert image to grayscale and apply morphological transformation filter not convert the result to binary image. Then, blurs the binary image using Gaussian filter and find all contour in image

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16

Liu, Ze, Yingfeng Cai, Long Chen, Hai Wang, and Youguo He. "Vehicle license plate recognition method based on deep convolution network in complex road scene." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 9 (2019): 2284–92. http://dx.doi.org/10.1177/0954407019851339.

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The license plate robust recognition algorithm in complex road scene has both theoretical and practical values. The existing license plate recognition algorithm can achieve better recognition results under ideal road scenes such as moderate light intensity, good shooting angle, and clear license plate target, but in complex road scenes such as fast speed, blurred aging of license plates, and low illumination such as rainy days, the effectiveness of the license plate recognition algorithm still needs to be improved. Based on the realistic requirements of license plate recognition algorithm and

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17

Yuan, Shuai, Guo Yun Zhang, Jian Hui Wu, and Long Yuan Guo. "Study of License Plate Recognition Technology." Advanced Materials Research 834-836 (October 2013): 1035–38. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1035.

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License plate recognition technology has been widely used with the development of intelligent traffic system, which studies vehicle identification based on digital image processing technology. This paper presents system design and realization of recognition system for license plate. License plate image is preprocessed by gradation and binaryzation at first, then the image noise caused by dirt is filtered by a mean value method. We adopt horizontal and vertical projection method to locate license plate. Character segmentation and recognition are carried out at last. Test result shows that the m

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18

Liu, Zhong Yan, Jian Yang, and Hong Mei Nie. "An Efficient Algorithm for License Plate Recognition." Applied Mechanics and Materials 278-280 (January 2013): 1297–300. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1297.

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The license plate recognition(LPR) is the key technology in intelligent transportation system. This paper discusses the whole process of license plate recognition technology, include the license plate image preprocessing, license plate location, character segmentation and character recognition, and simulated it by MATLAB. The experimental result show this method can obtain good recognition effect.

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19

Salimah, U., V. Maharani, and R. Nursyanti. "Automatic License Plate Recognition Using Optical Character Recognition." IOP Conference Series: Materials Science and Engineering 1115, no. 1 (2021): 012023. http://dx.doi.org/10.1088/1757-899x/1115/1/012023.

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20

Kyyko, V. "Matching Based Multistyle License Plate Recognition." Kibernetika i vyčislitelʹnaâ tehnika 2020, no. 1(199) (2020): 5–18. http://dx.doi.org/10.15407/kvt199.01.005.

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21

., Rinku Solanki. "THE AUTOMATIC LICENSE PLATE RECOGNITION (ALPR)." International Journal of Research in Engineering and Technology 02, no. 08 (2013): 353–59. http://dx.doi.org/10.15623/ijret.2013.0208055.

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22

SHAWKY, A., A. HAMDY, H. KESHK, and M. EL_ADAWY. "LICENSE PLATE RECOGNITION OF MOVING VEHICLE." JES. Journal of Engineering Sciences 37, no. 6 (2009): 1489–98. http://dx.doi.org/10.21608/jesaun.2009.128533.

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23

Jain, Pratiksha, Neha Chopra, and Vaishali Gupta. "Automatic License Plate Recognition using OpenCV." International Journal of Computer Applications Technology and Research 3, no. 12 (2014): 756–61. http://dx.doi.org/10.7753/ijcatr0312.1001.

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24

Zeki Hamdey, Hanan. "License Plate Recognition for Security Places." JOURNAL OF EDUCATION AND SCIENCE 22, no. 3 (2009): 92–108. http://dx.doi.org/10.33899/edusj.2009.57754.

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25

Ma, Zhen, Jian Lei Li, and Xue Fei Tan. "Research on License Plate Recognition Technology." Applied Mechanics and Materials 44-47 (December 2010): 3667–71. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3667.

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With the growth in number of vehicle, intelligent vehicle management has become the research hotspot, and license plate automatic recognition system is also of great concern as a important technology of intelligent traffic system. In this paper, Hough transform used to edge extraction, tilt correction algorithm and character recognition based on Haursdorff distance are discussed. At last, the license plate recognition system is designed and implemented, and then the experimental result is analyzed.

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26

Randive, P. S., Sonam Bansod, Shruti Ahivale, Sonal Mohite, and Sneha Patil. "Automatic License Plate Recognition [ALPR] System." International Journal of Engineering Trends and Technology 35, no. 5 (2016): 224–27. http://dx.doi.org/10.14445/22315381/ijett-v35p248.

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27

Kuria, Betsy. "Lucrative Method for License Plate Recognition." IOSR Journal of Computer Engineering 2, no. 2 (2012): 37–38. http://dx.doi.org/10.9790/0661-0223738.

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28

Tiruneh, Embiale Merkebu, and De Ning Jiang. "Vehicle License Plate Registration Recognition System." Advanced Materials Research 718-720 (July 2013): 2286–90. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2286.

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Neural network had been used widely in many applications, such as to recognize an object or character, to detect a motion, to control a process, to forecast a result, to analyze data and for management of information. With the rapid growth of vehicles on the road and with the aid of improved technology, there is a demand for processing vehicles as conceptual resources in information systems. This paper will show how to design a system using the neural network to recognize the vehicle registration plate of vehicles. The approach to the project is by capturing footage and after which, the footag

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29

Zack, Lan, and Agnes Evert. "License Plate Recognition System Using MATLAB." DJ Journal of Advances in Electronics and Communication Engineering 1, no. 1 (2015): 29–33. http://dx.doi.org/10.18831/djece.org/2015011005.

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30

M. Merola, Linda, Cynthia Lum, Breanne Cave, and Julie Hibdon. "Community support for license plate recognition." Policing: An International Journal of Police Strategies & Management 37, no. 1 (2014): 30–51. http://dx.doi.org/10.1108/pijpsm-07-2012-0064.

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Purpose – Although the use of license plate recognition (LPR) technology by police is becoming increasingly common, no empirical studies have examined the legal or legitimacy implications of LPR. LPR may be used for a variety of purposes, ranging from relatively routine checks of stolen vehicles to more complex surveillance functions. The purpose of this paper is to develop a “continuum of LPR uses” that provides a framework for understanding the potential legal and legitimacy issues related to LPR. The paper then analyzes results from the first random-sample community survey on the topic. Des

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31

Anagnostopoulos, Christos-Nikolaos E. "License Plate Recognition: A Brief Tutorial." IEEE Intelligent Transportation Systems Magazine 6, no. 1 (2014): 59–67. http://dx.doi.org/10.1109/mits.2013.2292652.

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32

El-said, Shaimaa Ahmed. "Shadow aware license plate recognition system." Soft Computing 19, no. 1 (2014): 225–35. http://dx.doi.org/10.1007/s00500-014-1245-5.

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33

Hou, Xiang Hua, and Hong Hai Liu. "The Research and Improvement on Correction Algorithm of Inclination License Plate." Applied Mechanics and Materials 543-547 (March 2014): 2800–2803. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2800.

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More and more intelligent transportation technologies are applied to license plate detection and recognition that can greatly reduce the burden of traffic management. However, character segmentation of license plate is an indispensable step of license plate recognition. Traditional character segmentation algorithms of license plate mainly use the space between characters of license plate to segment characters, but the license plate cannot be recognized if there are degraded characters or license plate inclination. In this paper, an improved character segmentation algorithm of license plate is

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34

Shi, Gui Ming, Tong Wu, Hang Su, and Qing Tao Wei. "Research on Identification Technology of Vehicle License Plate Based on Image Processing." Applied Mechanics and Materials 513-517 (February 2014): 2827–30. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2827.

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Automatic vehicle license plate is an important part of intelligent transportation system. The success of the plate recognition will have a deep impact on the construction of intelligent transport systems. Image processing, tilt correction, character delimitation, character recognition and matching are main applications of vehicle license plate recognition, and the above process are implemented in matlab environment. Vehicle license plate location is implemented by vehicle license plate locating method based on edge detection and morphology filter in this article. The tilt correction mode base

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35

More, Nandan, and Bharat Tidke. "License Plate Recognition for Indian Number Plate: A Review." International Journal of Computer Applications 103, no. 15 (2014): 5–8. http://dx.doi.org/10.5120/18148-9391.

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36

Ran, Feng, Fa Yu Zhang, and Mei Hua Xu. "Research and Design of License Plate Recognition System." Applied Mechanics and Materials 556-562 (May 2014): 2623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2623.

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Introduce a complete system of license plate recognition: using morphological processing and priori knowledge of license plate to discern the location of license plate, accomplishing tilt correction through Radon transform, then fulfilling character segmentation of accurate positioning license plate by projection, finishing character recognition through BP neural network which was improved by the use of adaptive learning rate and momentum factor. With the programming and verification on Matlab experimental platform, experimental results show that we can have a preferable recognition speed and

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37

Wang, Zhongli, Xiping Ma, and Wenlin Huang. "Vehicle License Plate Recognition Based on Wavelet Transform and Vertical Edge Matching." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 06 (2019): 2050016. http://dx.doi.org/10.1142/s0218001420500160.

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With the improvement of our country’s economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicl

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38

Gao, Feng, Zhong Jian Dai, Kun Zhou, and Ya Ping Dai. "Research of License Plate Recognition under Complex Environment." Advanced Materials Research 989-994 (July 2014): 2569–75. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2569.

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In order to improve the license plate recognition accuracy under complex environment, a new license location algorithm combining vertical edge detection, color information of the license plate and mathematical morphology is presented in this paper. For balance of computing load and recognition accuracy, a “200-d” character feature rule is designed, and the “200-d” feature is used as the input of BP neural network to recognize the characters. Based on the above-mentioned methods, a license plate recognition system is set up, which can locate and recognize the license plate effectively, even whe

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39

Teng, Xiu Hua. "The Application of Image Processing Technology in the Intelligent Transportation System." Applied Mechanics and Materials 543-547 (March 2014): 2678–80. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2678.

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Image processing-based vehicle recognition is one of the important research fields in ITS. The existing methods are all based on license plate recognition and car shape recognition. Their common problem is algorithm stability. And the license plates are easy to be changed. All information about vehicles should be used to recognize them reliably. A problem to be solved is to find a method to recognize vehicles besides license plate recognition and vehicle model recognition. Vehicle license plate location and character segmentation are critical steps in the license plate recognition system, and

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40

Huang, Jianping. "Research on License Plate Image Segmentation and Intelligent Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 06 (2019): 2050014. http://dx.doi.org/10.1142/s0218001420500147.

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With the accumulation of people’s wealth and the improvement of purchasing power, more and more people are buying cars as a means of travel. Walking and cycling of the past have now become a car trip. License plate recognition technology is especially important in intelligent transportation systems. It has been widely used in large shopping malls or supermarket parking lots, highway toll stations, speeding violation supervision and other fields. However, the accuracy and efficiency of license plate image recognition are insufficient. To solve the above problems, we propose a license plate char

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41

Zhai, Weifang, Terry Gao, and Juan Feng. "Research on Pre-Processing Methods for License Plate Recognition." International Journal of Computer Vision and Image Processing 11, no. 1 (2021): 47–79. http://dx.doi.org/10.4018/ijcvip.2021010104.

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The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate ar

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42

CHEN, TIAN-DING. "LICENSE-PLATE RECOGNITION USING DWT AND NEURAL NETWORK." International Journal of Wavelets, Multiresolution and Information Processing 04, no. 04 (2006): 601–15. http://dx.doi.org/10.1142/s0219691306001488.

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This paper presents a new approach for license-plate recognition using Discrete Wavelet Transform (DWT) and Plastic Perception Neural Network (PPNN). It accomplishes the preliminary license-plate localization by applying low-pass wavelet coefficients. Since the amount of data reduces to 1/4, this approach saves a lot of running time, simplifies computational complexity, and economizes memory usage. It adopts the LL and HH sub-bands, which come from a two-dimensional Haar DWT to implement the localization and segmentation for license plates. The proposed methodology provides high accuracy for l

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43

Azad, Babak, and Eslam Ahmadzade. "Real-Time Multiple License Plate Recognition System." International Journal of Research in Computer Science 4, no. 2 (2014): 11–17. http://dx.doi.org/10.7815/ijorcs.42.2014.080.

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44

Omran, Safaa S., and Jumana A. Jarallah. "Iraqi Car License Plate Recognition Using OCR." Cihan University-Erbil Scientific Journal 2017, Special-1 (2017): 13–24. http://dx.doi.org/10.24086/cuesj.si.2017.n1a2.

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45

., Saranya K. "LICENSE PLATE RECOGNITION FOR TOLL PAYMENT APPLICATION." International Journal of Research in Engineering and Technology 03, no. 03 (2014): 713–17. http://dx.doi.org/10.15623/ijret.2014.0303130.

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46

SHIO, Akio, Yasuko TAKAHASHI, and Ken-ichiro ISHII. "License Plate Recognition Based on Image Processing." Journal of the Japan Society for Precision Engineering 57, no. 8 (1991): 1358–61. http://dx.doi.org/10.2493/jjspe.57.1358.

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47

Gilly, Divya, and Kumudha Raimond. "A Survey on License Plate Recognition Systems." International Journal of Computer Applications 61, no. 6 (2013): 34–40. http://dx.doi.org/10.5120/9934-4569.

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48

Yu, Yuan Chih, Shing Chern D. You, and Dwen Ren Tsai. "Hill Climbing Algorithm for License Plate Recognition." Advanced Materials Research 267 (June 2011): 995–1000. http://dx.doi.org/10.4028/www.scientific.net/amr.267.995.

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Histogram thresholding has been widely used for image processing—it is simple, fast, and computationally inexpensive. In this paper, we develop a creative approach based on histogram’s distributions to segment interest regions from background. Unlike the existing threshold detection methods which measure the statistics of histogram in the multi-modal images, our approach analyses the shape representation of multi-modal which has several hill-climbing curves. The behavior of algorithm works like human vision which focuses on the high contrast areas and scans the shape variation first. Moreover,

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49

Lee, Jae-Hyeon, Sung-Man Cho, Seung-Ju Lee, Cheong-Hwa Kim, and Goo-Man Park. "License Plate Recognition System Using Synthetic Data." Journal of the Institute of Electronics and Information Engineers 57, no. 1 (2020): 107–15. http://dx.doi.org/10.5573/ieie.2020.57.1.107.

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50

Chowdhury, Md Burhan Uddin. "Detection and Recognition of Bangladeshi License Plate." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (2020): 3734–40. http://dx.doi.org/10.30534/ijatcse/2020/187932020.

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