The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Automatic License Plate Recognition (ALPR): A State-of-the-Art Review
Abstract
Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.
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Cites
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,398
Morphological Image Analysis: Principles and Applications
(2000) · Sensor Review · doi:10.1108/sr.2000.08720cae.001 · Cited by 3,537
A fast marching level set method for monotonically advancing fronts.
Sethian, J. A. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.4.1591 · Cited by 3,221
Soille, P. (1999) · Springer eBooks · doi:10.1007/978-3-662-03939-7 · Cited by 1,977
Jähne, B. (1993) · Springer eBooks · doi:10.1007/978-3-662-21817-4 · Cited by 1,139
Automatic license plate recognition
Chang, S.-L., Chen, L.-S., Chung, Y.-C., & Chen, S.-W. (2004) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2004.825086 · Cited by 791
A License Plate-Recognition Algorithm for Intelligent Transportation System Applications
Anagnostopoulos, C. N. E., Anagnostopoulos, I. E., Loumos, V., & Kayafas, E. (2006) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2006.880641 · Cited by 736
License Plate Recognition From Still Images and Video Sequences: A Survey
Anagnostopoulos, C.-N. E., Anagnostopoulos, I. E., Psoroulas, I. D., Loumos, V., & Kayafas, E. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.922938 · Cited by 645
A method of combining multiple experts for the recognition of unconstrained handwritten numerals
Huang, Y. S., & Suen, C. Y. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.368145 · Cited by 564
An efficient method of license plate location
Zheng, D., Zhao, Y., & Wang, J. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.04.014 · Cited by 372
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A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance
Liu, X., Liu, W., Mei, T., & Ma, H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_53 · Cited by 641
Pseudonym Schemes in Vehicular Networks: A Survey
Petit, J., Schaub, F., Feiri, M., & Kargl, F. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2345420 · Cited by 434
Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks
Li, H., Wang, P., & Shen, C. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2847291 · Cited by 346
LAVEA: latency-aware video analytics on edge computing platform
Yi, S., Hao, Z., Zhang, Q., Zhang, Q., Shi, W., & Li, Q. (2017) · Proceedings of the Second ACM/IEEE Symposium on Edge Computing · doi:10.1145/3132211.3134459 · Cited by 244
Bulan, O., Kozitsky, V., Ramesh, P., & Shreve, M. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2639020 · Cited by 206
Metadata
- Authors
- Du, S., Ibrahim, M., Shehata, M., & Badawy, W.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2013-02
- Volume
- 23
- Issue
- 2
- Pages
- 311-325
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2102608210
- Cited by
- 835
- References
- 69
- FWCI
- 77.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Du, S., Ibrahim, M., Shehata, M., & Badawy, W. (2013). Automatic License Plate Recognition (ALPR): A State-of-the-Art Review. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2012.2203741
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