Automatic Localization of License Plate for Car in Wolfram Mathematica

Modern imaging devices make it possible to solve a complex of technical applied problems that require the synthesis and analysis of computer processing methods using threshold binarization, image classification, clustering, and the use of machine  learning to determine  areas of interest.  Thus,  segmentation  algorithms are widely  used for processing  medical  images. Computer technologies are used for the functioning of the intellectual environment, which allows to analyze the state of human health. The development of microelectronics makes it possible to increase the complexity of the applied image processing algorithms used to solve applied engineering problems. The issues of segmentation, pattern recognition, description and presentation of details, morphological analysis of images obtained by industrial equipment are widely discussed in the literature. For example, theories of optical signal processing taking into account interference, issues of image perception and analysis are presented in detail in domestic and foreign literature. The paper describes the  developed algorithm for localizing a car license plate, implemented in the Wolfram Mathematica system. First, the region of interest is determined, isolated from the rest of the image for its subsequent processing. An image representation is implemented using an affine transformation. Further segmentation of the characters on the license plate allows the characters to be identified. In the Mathematica system, a program code for the car license plate localization  algorithm  for its further recognition  has been  developed. The solution to the problem was obtained using the step-by-step application of the built-in and user-defined functions of the Wolfram Mathematica system. The algorithm has been tested on a representative sample of images. The average error did not exceed 10 %, which is in line with modern industrial image processing algorithms. The resulting car license plate identification algorithm can be used in digital devices to automatically determine and further image processing.

1. Blanchet G., Charbit M. (2006) Digital Signal and Image Processing Using Matlab. London: Science Europe. 764. https://doi.org/10.1002/9780470612385

2. Breton V., Dean K., Solomonides T. (2005) The Healthgrid White Paper. Studies in Health Technology and Informatics, 112, 249–318.

3. Miroshnikov М. М. (2010) Theoretical Foundations of Optoelectronic Devices. Saint- Petersburg, Lan Publ. 704 (in Russian).

4. Selyankin V. V., Skorokhod S. V. (2015) Image Analysis and Processing in Computer Vision Problems. Taganrog, Publishing House of Southern Federal University. 82 (in Russian).

5. Sabih D. E., Sabih A., Sabih Q., Khan A. N. (2011). Image perception and interpretation of abnormalities; can we believe our eyes? Can we do something about it?. Insights into imaging, 2 (1), 47–55. https://doi.org/10.1007/s13244-010-0048-1

6. Liu J., E. Lughofer, Zeng X. (2015) Aesthetic Perception of Visual Textures: a Holistic Exploration Using Texture Analysis, Psychological Experiment, and Perception Modeling. Front Comput Neurosci, (9), 1–14. https://doi.org/10.3389/fncom.2015.00134

7. Gonzalez R. C., Woods R. E. (2002) Digital Image Processing. New Jersey, Prentice Hall, Upper Saddlr River. 797 (in Russian).

8. Bribiesca E. (2000) A Chain Code for Representing 3-D Curves. Pattern Recog, 33 (5), 755–765. https://doi.org/10.1016/S0031-3203(99)00093-X

9. Rudakov P. I., Safonov I. V. (2000) Processing of Signals and Images. MATLAB 5.х. Moscow, Dialog-MEPhI Publ. 416 (in Russian).

10. Weihua W. (2009) License Plate Recognition Algorithm Based on Radial Basis Function Neural Networks. Intelligent Ubiquitous Computing and Education, International Symposium, 38–41. https://doi.org/10.1109/IUCE.2009.20

11. Patel S. G. (2013) Vehicle License Plate Recognition Using Morphology and Neural Network. International Journal on Cybernetics & Informatics, 2 (1), 1–7. https://doi.org/10.5121/ijci.2013.2101

12. Reshma P. (2012) Noise Removal and Blob Detection Approach for Number Plate Identification. International Journal of Computer Applications, 47 (8), 13–16. https://doi.org/10.5120/7207-9992

13. Bartolome L. S., Bandala A. A., Llorente C., Dadios E. P. (2012) Vehicle Parking Inventory System Utilizing Image Recognition Through Artificial Neural Networks. IEEE Region 10 Conference, 1–5. https://doi.org/10.1109/tencon.2012.6412301

14. Draghici S. (1997) A Neural Network Based Artificial Vision System for License Plate Recognition. International Journal of Neural Systems, 8 (1), 113–126. https://doi.org/10.1142/S0129065797000148

15. Anagnostopoulos C. N. E., Anagnostopoulos I. E., Psoroulas I. D., Loumos V., Kayafas E. (2008) License Plate Recognition from Still Images and Video Sequences: a Survey. IEEE Transactions on Intelligent Transportation Systems, 9 (3), 377–391. https://doi.org/10.1109/TITS.2008.922938

16. Yamaguchi K., Nagaya Y., Ueda K., Nemoto H., Nakagawa M. (1999) A Method for Identifying Specific Vehicles Using Template Atching. Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, 8–13. https://doi.org/10.1109/ITSC.1999.821019

17. Lotufo R. A., Morgan A. D., Johnson A. S. (2013) Automatic License Plate Recognition (ALPR) a State-of-the-Art Review. IEEE Transaction on Circuits and System for Video Technology, 23 (2), 311–325. https://doi.org/10.1109/TCSVT.2012.2203741

18. Hegt H. A., Dela Haye R. J., Khan N. A. (1998) A High Performance License Plate Recognition System. SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics, 5, 4357–4362. https://doi.org/10.1109/ICSMC.1998.727533

19. Anagnostopoulos C. N. E., Anagnostopoulos I. E., Loumos V., Kayafas E. (2006) A License Plate-Recognition Algorithm for Intelligent Transportation System Applications. IEEE Transactions on Intelligent Transportation Systems, 7 (3), 377–392. https://doi.org/10.1109/TITS.2006.880641

20. Comelli P., Ferragina P., Granieri M. N., Stabile F. (1995) Optical Recognition of Motor Vehicle License Plates. IEEE Transactions on Vehicular Technology, 44 (4), 790–799. https://doi.org/10.1109/25.467963

21. Jiao J. B., Ye Q. X., Huang Q. M. (2009) A Configurable Method for Multi-Style License Plate Recognition. Pattern Recognition, 42 (3), 358–369. https://doi.org/10.1016/j.patcog.2008.08.016

22. Goritov A. N. (108) Preliminary Image Processing in Technical Vision Systems. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki = Proceedings of TUSUR University, 21 (4), 70–82 (in Russian).