A Systematic Review Of Current Methods And Challenges In Automatic Number Plate Recognition
DOI:
https://doi.org/10.52783/jns.v14.2910Keywords:
Automatic Number plate recognition, Character segmentation, recognition, machine learning, computer vision, deep learningAbstract
Recent years have witnessed an unprecedented increase in the number of automobiles that have been put to use thereby leading to the design and development of a monitoring system like Automated Number Plate Recognition (ANPR) to deal with several tasks like law and order, supervision, and highway kiosk related operations. The functioning pattern as well as the requirement specification of these structures is varied due to multiple disparities encountered in the proposed systems. To substantiate the case in point, the majority of these applications are executed on smartphones or any other handheld gadgets on cloud servers, maneuvered in poorly illuminated, unfavourable weather situations. There have been numerous approaches that have been designed to implement ANPR that can efficiently suit the aforementioned real-time requirements. Generally, these systems have a pre-defined framework whose major tasks include the detection of license plates, character segmentation, and recognition of individual characters. Owing to the diversity of license plate formats and constraints with respect to environmental conditions, efficiency and preciseness of recognition highly rely on an accurate detection process. This research investigates several methods and techniques utilized in ANPR systems and presents a detailed review of them as a literature survey. Our objective is thus to facilitate towards beneficial analysis, and categorization of associated studies in the domain of ANPR and recognize the critical challenges encountered by scholars and application developers. Moreover, we also intend to present relevant research directions and a comprehensive analysis of modern real-time systems that can be employed in future ventures along with commendations to optimize the existing solutions to enable them to work under intense situations.
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Padmasiri, H., Shashirangana, J., Meedeniya, D., Rana, O., & Perera, C. (2022). Automated license plate recognition for resource-constrained environments. Sensors, 22(4), 1434.
A. Rio-Alvarez, J. de Andres-Suarez, M. Gonzalez-Rodriguez, D. Fernandez-Lanvin, and B. L. Pérez, ‘‘Effects of challenging weather and illumination on learning-based license plate detection in noncontrolled environments,’’ Sci. Program., vol. 2019, pp. 1–16, Jun. 2019.
Y.-T. Chen, J.-H. Chuang, W.-C. Teng, H.-H. Lin, and H.-T. Chen, ‘‘Robust license plate detection in nighttime scenes using multiple intensity IR-illuminator,’’ in Proc. IEEE Int. Symp. Ind. Electron., May 2012, pp. 893–898.
K. S. Raghunandan, P. Shivakumara, H. A. Jalab, R. W. Ibrahim, G. H. Kumar, U. Pal, and T. Lu, ‘‘Riesz fractional based model for enhancing license plate detection and recognition,’’ IEEE Trans. Circuits Syst. Video Technol., vol. 28, no. 9, pp. 2276–2288, Sep. 2018
S. Du, M. Ibrahim, M. Shehata, and W. Badawy, ‘‘Automatic license plate recognition (ALPR): A state-of-the-art review,’’ IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 2, pp. 311–325, Feb. 2013.
C.-N.-E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, V. Loumos, and E. Kayafas, ‘‘License plate recognition from still images and video sequences: A survey,’’ IEEE Trans. Intell. Transp. Syst., vol. 9, no. 3, pp. 377–391, Sep. 2008
M. Sarfraz, M. J. Ahmed, and S. A. Ghazi, ‘‘Saudi arabian license plate recognition system,’’ in Proc. Int. Conf. Geometric Modeling Graph., 2003, pp. 36–41
L. Luo, H. Sun, W. Zhou, and L. Luo, ‘‘An efficient method of license plate location,’’ in Proc. 1st Int. Conf. Inf. Sci. Eng., 2009, pp. 770–773.
C. Busch, R. Domer, C. Freytag, and H. Ziegler, ‘‘Feature based recognition of traffic video streams for online route tracing,’’ in Proc. 48th IEEE Veh. Technol. Conf. Pathway Global Wireless Revolution (VTC), vol. 3, May 1998, pp. 1790–1794.
Z. Sanyuan, Z. Mingli, and Y. Xiuzi, ‘‘Car plate character extraction under complicated environment,’’ in Proc. IEEE Int. Conf. Syst., Man Cybern., vol. 5, Oct. 2004, pp. 4722–4726.
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 Proc. IEEE Annu. Meeting Fuzzy Inf. Process. (NAFIPS), vol. 1, Jun. 2004, pp. 357–362.
W. Jia, H. Zhang, X. He, and Q. Wu, ‘‘Gaussian weighted histogram intersection for license plate classification,’’ in Proc. 18th Int. Conf. Pattern Recognit. (ICPR), vol. 3, 2006, pp. 574–577.
W. Jia, H. Zhang, X. He, and M. Piccardi, ‘‘Mean shift for accurate license plate localization,’’ in Proc. IEEE Intell. Transp. Syst., Sep. 2005, pp. 566–571.
F. Wang, L. Man, B. Wang, Y. Xiao, W. Pan, and X. Lu, ‘‘Fuzzy-based algorithm for color recognition of license plates,’’ Pattern Recognit. Lett., vol. 29, no. 7, pp. 1007–1020, 2008.
D.-S. Kim and S.-I. Chien, ‘‘Automatic car license plate extraction using modified generalized symmetry transform and image warping,’’ in Proc. IEEE Int. Symp. Ind. Electron. (ISIE), vol. 3, Jun. 2001, pp. 2022–2027.
H.-K. Xu, F.-H. Yu, J.-H. Jiao, and H.-S. Song, ‘‘A new approach of the vehicle license plate location,’’ in Proc. 6th Int. Conf. Parallel Distrib. Comput. Appl. Technol. (PDCAT), 2005, pp. 1055–1057.
R. Zunino and S. Rovetta, ‘‘Vector quantization for license-plate location and image coding,’’ IEEE Trans. Ind. Electron., vol. 47, no. 1, pp. 159–167, Feb. 2000.
C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas, ‘‘A license plate-recognition algorithm for intelligent transportation system applications,’’ IEEE Trans. Intell. Transp. Syst., vol. 7, no. 3, pp. 377–392, Sep. 2006.
H. Caner, H. S. Gecim, and A. Z. Alkar, ‘‘Efficient embedded neuralnetwork-based license plate recognition system,’’ IEEE Trans. Veh. Technol., vol. 57, no. 5, pp. 2675–2683, Sep. 2008.
C.-T. Hsieh, Y.-S. Juan, and K.-M. Hung, ‘‘Multiple license plate detection for complex background,’’ in Proc. 19th Int. Conf. Adv. Inf. Netw. Appl. (AINA), vols. 1–2, 2005, pp. 389–392.
L. Zou, M. Zhao, Z. Gao, M. Cao, H. Jia, and M. Pei, ‘‘License plate detection with shallow and deep CNNs in complex environments,’’ Complexity, vol. 2018, pp. 1–6, Dec. 2018
S. Du, M. Ibrahim, M. Shehata, and W. Badawy, ‘‘Automatic license plate recognition (ALPR): A state-of-the-art review,’’ IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 2, pp. 311–325, Feb. 2013.
H. Li, P. Wang, and C. Shen, ‘‘Toward end-to-end car license plate detection and recognition with deep neural networks,’’ IEEE Trans. Intell. Transp. Syst., vol. 20, no. 3, pp. 1126–1136, Mar. 2019.
Z. Xu, W. Yang, A. Meng, N. Lu, H. Huang, C. Ying, and L. Huang, ‘‘Towards end-to-end license plate detection and recognition: A large dataset and baseline,’’ in Proc. Eur. Conf. Comput. Vis., in Lecture Notes in Computer Science, vol. 11217, 2018, pp. 261–277.
K. Simonyan and A. Zisserman, ‘‘Very deep convolutional networks for large-scale image recognition,’’ 2014, arXiv:1409.1556. [Online]. Available: http://arxiv.org/abs/1409.1556
S. Ren, K. He, R. Girshick, and J. Sun, ‘‘Faster R-CNN: Towards real-time object detection with region proposal networks,’’ in Proc. Adv. Neural Inf. Process. Syst., 2015, pp. 91–99.
R. Girshick, ‘‘Fast R-CNN,’’ in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2015, pp. 1440–1448.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, ‘‘Object detectors emerge in deep scene CNNs,’’ 2014, arXiv:1412.6856.
A. M. Al-Ghaili, S. Mashohor, A. Ismail, and A. R. Ramli, ‘‘A new vertical edge detection algorithm and its application,’’ in Proc. Int. Conf. Comput. Eng. Syst., Nov. 2008, pp. 204–209.
G. Heo, M. Kim, I. Jung, D.-R. Lee, and I.-S. Oh, ‘‘Extraction of car license plate regions using line grouping and edge density methods,’’ in Proc. Int. Symp. Inf. Technol. Converg. (ISITC), Nov. 2007, pp. 37–42.
H.-J. Lee, S.-Y. Chen, and S.-Z. Wang, ‘‘Extraction and recognition of license plates of motorcycles and vehicles on highways,’’ in Proc. 17th Int. Conf. Pattern Recognit. (ICPR), vol. 4, 2004, pp. 356–359.
J. Matas and K. Zimmermann, ‘‘Unconstrained licence plate and text localization and recognition,’’ in Proc. IEEE Intell. Transp. Syst., Sep. 2005, pp. 225–230.
S. Draghici, ‘‘A neural network based artificial vision system for licence plate recognition,’’ Int. J. Neural Syst., vol. 8, no. 1, pp. 113–126, 1997.
B. K. Cho, S. H. Ryu, D. R. Shin, and J. I. Jung, ‘‘License plate extraction method for identification of vehicle violations at a railway level crossing,’’ Int. J. Automot. Technol., vol. 12, no. 2, pp. 281–289, Apr. 2011.
X. Zhang, P. Shen, Y. Xiao, B. Li, Y. Hu, D. Qi, X. Xiao, and L. Zhang, ‘‘License plate-location using AdaBoost algorithm,’’ in Proc. IEEE Int. Conf. Inf. Automat., Jun. 2010, pp. 2456–2461.
Q. Wu, H. Zhang, W. Jia, X. He, J. Yang, and T. Hintz, ‘‘Car plate detection using cascaded tree-style learner based on hybrid object features,’’ in Proc. IEEE Int. Conf. Video Signal Based Surveill., Nov. 2006, p. 15.
H. Zhang, W. Jia, X. He, and Q. Wu, ‘‘Learning-based license plate detection using global and local features,’’ in Proc. 18th Int. Conf. Pattern Recognit. (ICPR), vol. 2, 2006, pp. 1102–1105.
S.-Z. Wang and H.-J. Lee, ‘‘A cascade framework for a real-time statistical plate recognition system,’’ IEEE Trans. Inf. Forensics Security, vol. 2, no. 2, pp. 267–282, Jun. 2007.
Z. Selmi, M. B. Halima, and A. M. Alimi, ‘‘Deep learning system for automatic license plate detection and recognition,’’ in Proc. 14th IAPR Int. Conf. Document Anal. Recognit. (ICDAR), Nov. 2017, pp. 1132–1138.
Y. Zhong and A. Jain, ‘‘Object localization using color, texture and shape,’’ Pattern Recognit., vol. 33, pp. 277–294, Apr. 2006.
M. Mirmehdi and M. Petrou, ‘‘Segmentation of color textures,’’ IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 2, pp. 142–159, Feb. 2000.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, ‘‘You only look once: Unified, real-time object detection,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 779–788
X. Xu, Z. Wang, Y. Zhang, and Y. Liang, ‘‘A method of multi-view vehicle license plates location based on rectangle features,’’ in Proc. 8th Int. Conf. Signal Process., vol. 3, 2006, pp. 1–4.
M.-S. Pan, J.-B. Yan, and Z.-H. Xiao, ‘‘Vehicle license plate character segmentation,’’ Int. J. Autom. Comput., vol. 5, no. 4, pp. 425–432, Oct. 2008.
M.-S. Pan, Q. Xiong, and J.-B. Yan, ‘‘A new method for correcting vehicle license plate tilt,’’ Int. J. Autom. Comput., vol. 6, no. 2, pp. 210–216, May 2009.
T. Nukano, M. Fukumi, and M. Khalid, ‘‘Vehicle license plate character recognition by neural networks,’’ in Proc. Int. Symp. Intell. Signal Process. Commun. Syst. (ISPACS), 2004, pp. 771–775.
B.-F. Wu, S.-P. Lin, and C.-C. Chiu, ‘‘Extracting characters from real vehicle licence plates out-of-doors,’’ IET Comput. Vis., vol. 1, no. 1, pp. 2–10, Mar. 2007
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. Neural Netw., vol. 4, Jul. 2004, pp. 2965–2970.
B.-G. Han, J. T. Lee, K.-T. Lim, and D.-H. Choi, ‘‘License plate image generation using generative adversarial networks for end-to-end license plate character recognition from a small set of real images,’’ Appl. Sci., vol. 10, no. 8, p. 2780, Apr. 2020.
S. Montazzolli and C. Jung, ‘‘Real-time Brazilian license plate detection and recognition using deep convolutional neural networks,’’ in Proc. 30th SIBGRAPI Conf. Graph., Patterns Images, Oct. 2017, pp. 55–62.
R. Laroca, A. L. Zanlorensi, R. G. Gonçalves, E. Todt, W. R. Schwartz, and D. Menotti, ‘‘An efficient and layout-independent automatic license plate recognition system based on the YOLO detector,’’ Sep. 2019, arXiv:1909.01754.
C. A. Rahman, W. Badawy, and A. Radmanesh, ‘‘A real time vehicle’s license plate recognition system,’’ in Proc. IEEE Conf. Adv. Video Signal Based Surveill., Jul. 2003, pp. 163–166.
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 Proc. Iberian Conf. Pattern Recognit. Image Anal. Berlin, Germany: Springer, 2005, pp. 571–578.
[54] H. A. Hegt, R. J. de la Haye, and N. A. Khan, ‘‘A high performance license plate recognition system,’’ in Proc. IEEE Int. Conf. Syst., Man, Cybern. (SMC), Oct. 1998, pp. 4357–4362.
P. Hu, Y. Zhao, Z. Yang, and J. Wang, ‘‘Recognition of gray character using Gabor filters,’’ in Proc. 5th Int. Conf. Inf. Fusion (FUSION), vol. 1, 2002, pp. 419–424.
S. N. H. S. Abdullah, M. Khalid, R. Yusof, and K. Omar, ‘‘License plate recognition using multi-cluster and multilayer neural networks,’’ in Proc. 2nd Int. Conf. Inf. Commun. Technol., vol. 1, 2006, pp. 1818–1823.
Shashirangana, Jithmi & Padmasiri, Heshan & Meedeniya, Dulani & Perera, Charith. (2020). Automated License Plate Recognition: A Survey on Methods and Techniques. IEEE Access. 9. 11203-11225. 10.1109/ACCESS.2020.3047929.
Y. Yuan, W. Zou, Y. Zhao, X. Wang, X. Hu, and N. Komodakis, ‘‘A robust and efficient approach to license plate detection,’’ IEEE Trans. Image Process., vol. 26, no. 3, pp. 1102–1114, Mar. 2017.
G.-S. Hsu, J.-C. Chen, and Y.-Z. Chung, ‘‘Application-oriented license plate recognition,’’ IEEE Trans. Veh. Technol., vol. 62, no. 2, pp. 552–561, Feb. 2013.
W. Zhou, H. Li, Y. Lu, and Q. Tian, ‘‘Principal visual word discovery for automatic license plate detection,’’ IEEE Trans. Image Process., vol. 21, no. 9, pp. 4269–4279, Sep. 2012.
D.-J. Kang, ‘‘Dynamic programming-based method for extraction of license plate numbers of speeding vehicles on the highway,’’ Int. J. Automot. Technol., vol. 10, no. 2, pp. 205–210, Apr. 2009.
Kashyap, A.; Suresh, B.; Patil, A.; Sharma, S.; Jaiswal, A. Automatic number plate recognition. In Proceedings of the Communication Control and Networking (ICACCCN), Greater Noida, India, 12–13 October 2018; pp. 838–843
Vaishnav, A.; Mandot, M. An integrated automatic number plate recognition for recognizing multi language fonts. In Proceedings of the 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), Noida, India, 29–31 August 2018; pp. 551–556.
Sferle, R.M.; Moisi, E.V. Automatic Number Plate Recognition for a Smart Service Auto. In Proceedings of the 2019 15th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania, 13–14 June 2019; pp. 57–60.
Desai, G.G.; Bartakke, P.P. Real-Time Implementation Of Indian License Plate Recognition System. In Proceedings of the 2018 IEEE Punecon, Pune, India, 30 November 2018; pp. 1–5.
Sasi, A.; Sharma, S.; Cheeran, A.N. Automatic car number plate recognition. In Proceedings of the 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 17–18 March 2017; pp. 1–6.
Singh, V.; Srivastava, A.; Kumar, S.; Ghosh, R. A Structural Feature Based Automatic Vehicle Classification System at Toll Plaza. In International Conference on Internet of Things and Connected Technologies; Springer: Jaipur, India, 9–10 May 2019; pp. 1–10.
Ahmad, I.S.; Boufama, B.; Habashi, P.; Anderson, W.; Elamsy, T. Automatic license plate recognition: A comparative study. In Proceedings of the 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Abu Dhabi, United Arab Emirates, 7–10 December 2015; pp. 635–640.
Mutholib, A.; Gunawan, T.S.; Kartiwi, M. Design and implementation of automatic number plate recognition on android platform. In Proceedings of the 2012 International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia, 3–5 July 2012; pp. 540–543.
Ashtari, A.H.; Nordin, M.J.; Fathy, M. An Iranian license plate recognition system based on color features. IEEE Trans. Intell. Transp. Syst. 2014, 15, 1690–1705.
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