Authors:
Tarun Kumar,DOI NO:
https://doi.org/10.26782/jmcms.2021.10.00004Keywords:
Automatic number plate recognition (ANPR),license plate detection (LPD),Edge detection,Texture detection,Abstract
Automatic Number Plate Recognition (ANPR) is an image processing technique that is used to extract the symbols (characters and digits) embedded on the number (license) plate to identify the vehicles. Huge numbers of ANPR techniques have been proposed by various researchers in the past. Most of the ANPR techniques are designed for restricted conditions due to the diversity of the license plate styles, environmental conditions etc. Not every technique is suited for all kinds of conditions. In general, the ANPR technique comprises of the following three stages; license plate detection (LPD); character segmentation; and character recognition. There exist a wide variety of techniques for carrying out each of the steps of the ANPR. Some score over others. This paper presents a State-of-the-Art survey of the various leading LPD techniques that exist today and an attempt has been made to summarize these techniques based on pros and cons and their limitations. Each technique is classified based on the features used at each stage of LPD. This survey shall help provide future direction towards the development of efficient and accurate techniques for ANPR. It shall also assist in identifying and shortlisting the methodologies that are best suited for the particular problem domain.Refference:
I. A. A. WANG, L. MAN, B. WANG, Y. XIAO, W. PAN, AND X. LU, “FUZZY-BASED ALGORITHM FOR COLOR RECOGNITION OF LICENSE PLATES,” PATTERN RECOGNITION LETTERS, VOL. 29, NO. 7, PP. 1007–1020, 2008.
II. “http://code.google.com/p/tesseract-ocr,” 2012.
III. A. Capar and M. Gokmen, “Concurrent segmentation and recognition with shape-driven fast marching methods,” in 18th International Conference on Pattern Recognition (ICPR’06), 2006, vol. 1, pp. 155–158.
IV. A. Rabee and I. Barhumi, “License plate detection and recognition in complex scenes using mathematical morphology and support vector machines,” in IWSSIP 2014 Proceedings, 2014, pp. 59–62.
V. B. R. Lee, K. Park, H. Kang, H. Kim, and C. Kim, “Adaptive local binarization method for recognition of vehicle license plates,” in International Workshop on Combinatorial Image Analysis, 2004, pp. 646–655.
VI. C. Busch, R. Domer, C. Freytag, and H. Ziegler, “Feature based recognition of traffic video streams for online route tracing,” in Vehicular Technology Conference, 1998. VTC 98. 48th IEEE, 1998, vol. 3, pp. 1790–1794.
VII. C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas, “A license plate-recognition algorithm for intelligent transportation system applications,” IEEE Transactions on Intelligent transportation systems, vol. 7, no. 3, pp. 377–392, 2006.
VIII. C. Patel, A. Patel, and D. Patel, “Optical character recognition by open source OCR tool tesseract: A case study,” International Journal of Computer Applications, vol. 55, no. 10, 2012.
IX. D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 5, pp. 603–619, 2002.
X. D. Llorens, A. Marzal, V. Palazón, and J. M. Vilar, “Car license plates extraction and recognition based on connected components analysis and HMM decoding,” in Iberian Conference on Pattern Recognition and Image Analysis, 2005, pp. 571–578.
XI. D. Zheng, Y. Zhao, and J. Wang, “An efficient method of license plate location,” Pattern Recognition Letters, vol. 26, no. 15, pp. 2431–2438, 2005.
XII. D.-J. Kang, “Dynamic programming-based method for extraction of license plate numbers of speeding vehicles on the highway,” International Journal of Automotive Technology, vol. 10, no. 2, pp. 205–210, 2009.
XIII. E. R. Lee, P. K. Kim, and H. J. Kim, “Automatic recognition of a car license plate using color image processing,” in Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference, 1994, vol. 2, pp. 301–305.
XIV. F. Martin, M. Garcia, and J. L. Alba, “New methods for automatic reading of VLP’s (Vehicle License Plates),” in Proc. IASTED Int. Conf. SPPRA, 2002, pp. 126–131.
XV. G. Henrich, “A simple computational method for reducing streak artifacts in CT images,” Computerized tomography, vol. 4, no. 1, pp. 67–71, 1980.
XVI. G. Li, R. Zeng, and L. Lin, “Research on vehicle license plate location based on neural networks,” in First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC’06), 2006, vol. 3, pp. 174–177.
XVII. G.-S. Hsu, J.-C. Chen, and Y.-Z. Chung, “Application-oriented license plate recognition,” IEEE transactions on vehicular technology, vol. 62, no. 2, pp. 552–561, 2013.
XVIII. H. Caner, H. S. Gecim, and A. Z. Alkar, “Efficient embedded neural-network-based license plate recognition system,” IEEE Transactions on Vehicular Technology, vol. 57, no. 5, pp. 2675–2683, 2008.
XIX. H. E. Kocer and K. K. Cevik, “Artificial neural networks based vehicle license plate recognition,” Procedia Computer Science, vol. 3, pp. 1033–1037, 2011.
XX. H. Liu and X. Ding, “Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes,” in Eighth International Conference on Document Analysis and Recognition (ICDAR’05), 2005, pp. 19–23.
XXI. I. Paliy, V. Turchenko, V. Koval, A. Sachenko, and G. Markowsky, “Approach to recognition of license plate numbers using neural networks,” in Proc. IEEE Int. Joint Conf. Neur. Netw, 2004, vol. 4, pp. 2965–2970.
XXII. I. Rish, “An empirical study of the naive Bayes classifier,” in IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001, vol. 3, no. 22, pp. 41–46.
XXIII. J. A. Sethian, “A fast marching level set method for monotonically advancing fronts,” Proceedings of the National Academy of Sciences, vol. 93, no. 4, pp. 1591–1595, 1996.
XXIV. J. Jiao, Q. Ye, and Q. Huang, “A configurable method for multi-style license plate recognition,” Pattern Recognition, vol. 42, no. 3, pp. 358–369, 2009.
XXV. J.-M. Guo and Y.-F. Liu, “License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques,” IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp. 1417–1424, 2008.
XXVI. K. K. Kim, K. Kim, J. Kim, and H. J. Kim, “Learning-based approach for license plate recognition,” in Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, 2000, vol. 2, pp. 614–623.
XXVII. K. Kanayama, Y. Fujikawa, K. Fujimoto, and M. Horino, “Development of vehicle-license number recognition system using real-time image processing and its application to travel-time measurement,” in Vehicular Technology Conference, 1991. Gateway to the Future Technology in Motion., 41st IEEE, 1991, pp. 798–804.
XXVIII. K. Miyamoto, K. Nagano, M. Tamagawa, I. Fujita, and M. Yamamoto, “Vehicle license-plate recognition by image analysis,” in Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON’91., 1991 International Conference on, 1991, pp. 1734–1738.
XXIX. K.-B. Kim, S.-W. Jang, and C.-K. Kim, “Recognition of car license plate by using dynamical thresholding method and enhanced neural networks,” in International Conference on Computer Analysis of Images and Patterns, 2003, pp. 309–319.
XXX. L. Juan and O. Gwun, “A comparison of sift, pca-sift and surf,” International Journal of Image Processing (IJIP), vol. 3, no. 4, pp. 143–152, 2009.
XXXI. L. Luo, H. Sun, W. Zhou, and L. Luo, “An efficient method of license plate location,” in 2009 First International Conference on Information Science and Engineering, 2009, pp. 770–773.
XXXII. L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” IEEE transactions on image processing, vol. 2, no. 2, pp. 176–201, 1993.
XXXIII. L. Zheng, X. He, B. Samali, and L. T. Yang, “An algorithm for accuracy enhancement of license plate recognition,” Journal of computer and system sciences, vol. 79, no. 2, pp. 245–255, 2013.
XXXIV. M. S. Landy and J. R. Bergen, “Texture segregation and orientation gradient,” Vision research, vol. 31, no. 4, pp. 679–691, 1991.
XXXV. M. Sarfraz, M. J. Ahmed, and S. A. Ghazi, “Saudi Arabian license plate recognition system,” in Geometric Modeling and Graphics, 2003. Proceedings. 2003 International Conference on, 2003, pp. 36–41.
XXXVI. M.-C. Su, H.-H. Chen, and W.-C. Cheng, “A neural-network-based approach to optical symbol recognition,” Neural processing letters, vol. 15, no. 2, pp. 117–135, 2002.
XXXVII. M.-L. Wang, Y.-H. Liu, B.-Y. Liao, Y.-S. Lin, and M.-F. Horng, “A vehicle license plate recognition system based on spatial/frequency domain filtering and neural networks,” in International Conference on Computational Collective Intelligence, 2010, pp. 63–70.
XXXVIII. N. Singh, “A Smart Framework for Identifying Road Traffic Violators,” in International Conference on “Computing for Sustainable Global Development, 2015.
XXXIX. P. Comelli, P. Ferragina, M. N. Granieri, and F. Stabile, “Optical recognition of motor vehicle license plates,” IEEE Transactions on Vehicular Technology, vol. 44, no. 4, pp. 790–799, 1995.
XL. P. Jackway, “Improved morphological top-hat,” Electronics Letters, vol. 36, no. 14, pp. 1194–1195, 2000.
XLI. P. Kulkarni, A. Khatri, P. Banga, and K. Shah, “Automatic Number Plate Recognition (ANPR) System for Indian conditions,” in Radioelektronika, 2009. RADIOELEKTRONIKA’09. 19th International Conference, 2009, pp. 111–114.
XLII. Q. Gao, X. Wang, and G. Xie, “License plate recognition based on prior knowledge,” in 2007 IEEE International Conference on Automation and Logistics, 2007, pp. 2964–2968.
XLIII. R. De Maesschalck, D. Jouan-Rimbaud, and D. L. Massart, “The mahalanobis distance,” Chemometrics and intelligent laboratory systems, vol. 50, no. 1, pp. 1–18, 2000.
XLIV. R. Smith, “An overview of the Tesseract OCR engine,” 2007.
XLV. S. Nomura, K. Yamanaka, O. Katai, H. Kawakami, and T. Shiose, “A novel adaptive morphological approach for degraded character image segmentation,” Pattern Recognition, vol. 38, no. 11, pp. 1961–1975, 2005.
XLVI. S. Rasheed, A. Naeem, and O. Ishaq, “Automated Number Plate Recognition using hough lines and template matching,” in Proceedings of the World Congress on Engineering and Computer Science, 2012, vol. 1, pp. 24–26.
XLVII. S. Yohimori, Y. Mitsukura, M. Fukumi, N. Akamatsu, and N. Pedrycz, “License plate detection system by using threshold function and improved template matching method,” in Fuzzy Information, 2004. Processing NAFIPS’04. IEEE Annual Meeting of the, 2004, vol. 1, pp. 357–362.
XLVIII. S.-L. Chang, L.-S. Chen, Y.-C. Chung, and S.-W. Chen, “Automatic license plate recognition,” IEEE transactions on intelligent transportation systems, vol. 5, no. 1, pp. 42–53, 2004.
XLIX. T. D. Duan, T. H. Du, T. V. Phuoc, and N. V. Hoang, “Building an automatic vehicle license plate recognition system,” in Proc. Int. Conf. Comput. Sci. RIVF, 2005, pp. 59–63.
L. T. Shuang-tong and L. Wen-ju, “Number and letter character recognition of vehicle license plate based on edge Hausdorff distance,” in Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT’05), 2005, pp. 850–852.
LI. T.-H. Wang, F.-C. Ni, K.-T. Li, and Y.-P. Chen, “Robust license plate recognition based on dynamic projection warping,” in Networking, Sensing and Control, 2004 IEEE International Conference on, 2004, vol. 2, pp. 784–788.
LII. W. Devapriya, C. N. K. Babu, and T. Srihari, “Indian License Plate Detection and Recognition Using Morphological Operation and Template Matching,” Evolution, vol. 3427.
LIII. W. Jia, H. Zhang, and X. He, “Region-based license plate detection,” Journal of Network and computer Applications, vol. 30, no. 4, pp. 1324–1333, 2007.
LIV. W. Zhou, H. Li, Y. Lu, and Q. Tian, “Principal visual word discovery for automatic license plate detection,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 4269–4279, 2012.
LV. X. Shi, W. Zhao, and Y. Shen, “Automatic license plate recognition system based on color image processing,” in International Conference on Computational Science and Its Applications, 2005, pp. 1159–1168.
LVI. Y. Cheng, J. Lu, and T. Yahagi, “Car license plate recognition based on the combination of principal components analysis and radial basis function networks,” in Signal Processing, 2004. Proceedings. ICSP’04. 2004 7th International Conference on, 2004, vol. 2, pp. 1455–1458.
LVII. Y. S. Soh, B. T. Chun, and H. S. Yoon, “Design of real time vehicle identification system,” in Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on, 1994, vol. 3, pp. 2147–2152.
LVIII. Y. Wen, Y. Lu, J. Yan, Z. Zhou, K. M. von Deneen, and P. Shi, “An algorithm for license plate recognition applied to intelligent transportation system,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 830–845, 2011.
LIX. Y. Yoon, K.-D. Ban, H. Yoon, and J. Kim, “Blob detection and filtering for character segmentation of license plates,” in Multimedia Signal Processing (MMSP), 2012 IEEE 14th International Workshop on, 2012, pp. 349–353.
LX. Y.-R. Wang, W.-H. Lin, and S.-J. Horng, “A sliding window technique for efficient license plate localization based on discrete wavelet transform,” Expert Systems with Applications, vol. 38, no. 4, pp. 3142–3146, 2011.
LXI. Z. Liu, A. Liu, C. Wang, and Z. Niu, “Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification,” Future Generation Computer Systems, vol. 20, no. 7, pp. 1119–1129, 2004.
LXII. Z. Qin, S. Shi, J. Xu, and H. Fu, “Method of license plate location based on corner feature,” in 2006 6th World Congress on Intelligent Control and Automation, 2006, vol. 2, pp. 8645–8649.