Journal of the Optical Society of Korea

Optical Society of Korea (한국광학회)
권호 표지
DOI QR코드

DOI QR Code

Precise Detection of Car License Plates by Locating Main Characters

  • Received : 2010.08.10
  • Accepted : 2010.09.07
  • Published : 2010.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

We propose a novel method to precisely detect car license plates by locating main characters, which are printed with large font size. The regions of the main characters are directly detected without detecting the plate region boundaries, so that license regions can be detected more precisely than by other existing methods. To generate a binary image, multiple thresholds are applied, and segmented regions are selected from multiple binarized images by a criterion of size and compactness. We do not employ any character matching methods, so that many candidates for main character groups are detected; thus, we use a neural network to reject non-main character groups from the candidates. The relation of the character regions and the intensity statistics are used as the input to the neural network for classification. The detection performance has been investigated on real images captured under various illumination conditions for 1000 vehicles. 980 plates were correctly detected, and almost all non-detected plates were so stained that their characters could not be isolated for character recognition. In addition, the processing time is fast enough for a commercial automatic license plate recognition system. Therefore, the proposed method can be used for recognition systems with high performance and fast processing.

Keywords

References

  1. S. W. Park, S. C. Hwang, and J. W. Park, “The extraction of vehicle license plate region using edge directional properties of wavelet subband,” IEICE Trans. Inf. & Syst. E86-D, 664-669 (2003).
  2. C.-N. E. Anagnostopoulos, I. E. Anagnostrpoulos, I. D. Psoroulas, V. Loumos, and E. Kayafas, “License plate recognition from still images and video sequences: a survey,” IEEE Trans. Intell. Transp. Syst. 9, 377-391 (2008). https://doi.org/10.1109/TITS.2008.922938
  3. W. Jia, H. Zhang, and X. He, “Region-based license plate detection,” J. Netw. Comput. Appl. 30, 1324-1333 (2007). https://doi.org/10.1016/j.jnca.2006.09.010
  4. W. Jia, X. He, and M. Piccardi, “Automatic license plate recognition: a review,” in Proc. Int. Conf. Imaging Science, Systems and Technology (Las Vegas, USA, Jun. 2004), pp. 43-49.
  5. D. S. Gao and J. Zhou, “Car license plates detection form complex scene,” in Proc. 5th Int. Conf. Signal Processing (Beijing, China, Aug. 2000), pp. 1409-1414.
  6. H. Zhang, W. Jia, X. He, and Q. Wu, “Real-time license plate detection under various conditions,” Lecture Notes in Computer Science 4159, 192-199 (2006). https://doi.org/10.1007/11833529_20
  7. V. Shapiro, D. Dimov, S. Bonchev, V. Velichkov, and G. Gluhchev, “Adaptive license plate image extraction,” in Proc. 5th Int. Conf. Computer Systems and Technologies (Rousse, Bulgaria, Jun. 2004), pp. 1-7.
  8. D. Zheng, Y. Zhao, and J. Wang, “An efficient method of license plate location,” Pattern Recognit. Lett. 26, 2431-2438 (2005). https://doi.org/10.1016/j.patrec.2005.04.014
  9. R. Parisi, E. D. D. Claudio, G. Lucarelli, and G. Orlandi, “Car plate recognition by neural networks and image processing,” in Proc. IEEE Int. Symp. Circuits and Systems (Monterey, USA, May 1998), pp. 195-198.
  10. 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 (Crete, Greece, Jun. 2002).
  11. B. Hongliang and L. Changping, “A hybrid license plate extraction method based on edge statistics and morphology,” in Proc. Int. Conf. Pattern Recognition (Cambridge, UK, Aug. 2004), pp. 831-834.
  12. S. H. M. Kasaei, S. M. M. Kasaei, and S. A. Monadjemi, “A novel morphological method for detection and recognition of vehicle license plates,” Amer. J. of Appl. Sci. 6, 2066-2070 (2009). https://doi.org/10.3844/ajassp.2009.2066.2070
  13. V. Abolghasemi and A. Ahmadyfard, “A fast algorithm for license plate detection,” Lecture Notes in Computer Science 4781, 468-477 (2007). https://doi.org/10.1007/978-3-540-76414-4_46
  14. Y. Yanamur, M. Goto, and D. Nishiyama, “Extraction and tracking of the license plate using Hough transform and voted block matching,” in Proc. IEEE Intelligent Vehicles Symp. (Hilton Hotel, Columbus, USA, Jun. 2003), pp. 9-11.
  15. M. Sarfraz, M. J. Ahmed, and S. A. Ghazi, “Saudi Arabian license plate recognition system,” in Proc. 2003 Int. Conf. Geometric Modeling and Graphics (London, England, Jul. 2003), pp. 36-41.
  16. M. Yu and Y. D. Kim, “An approach to Korean license plate recognition based on vertical edge matching,” in Proc 2000 IEEE Int. Conf. Systems, Man, and Cybernetics (Nashville, USA, Oct. 2000), pp. 2975-2980.
  17. T. D. Duan, T. L. H. Du, T. V. Phuoc, and N. V. Hoang, “Building an automatic vehicle license-plate recognition system,” in Proc Int. Conf. Computer Science (Can Tho, Vietnam, Feb. 2005), pp. 59-63.
  18. K. I. Kim, K. Jung, and J. H. Kim, “Color texture-based object detection: an application to license plate localization,” Lecture Notes in Computer Science 2388, 321-335 (2002). https://doi.org/10.1007/3-540-45665-1_25
  19. S. K. Kim, D. W. Kim, and H. J. Kim, “A recognition of vehicle license plate using a genetic algorithm based segmentation,” in Proc. Int. Conf. Image Processing (Lausanne, Switzerland, Sep. 1996), pp. 661-664.
  20. G. Cao, J. Chen, and J. Jiang, “An adaptive approach to vehicle license plate localization,” in Proc 29th Annual Conf. IECON (Hotel Roanoke and Conference Center, USA, Nov. 2003), pp. 1786-1791.
  21. K. Deb, H. Lim, S.-J. Kang, and K.-H. Jo, “An efficient method of vehicle plate detection based on HSI color model and histogram,” Lecture Notes in Computer Science 5579, 66-75 (2009). https://doi.org/10.1007/978-3-642-02568-6_7
  22. S.-L. Chang, L.-S. Chen, Y.-C. Chung, and S.-W. Chen, “Automatic license plate recognition,” IEEE Trans. Intell. Transp. Syst. 5, 42-53 (2004). https://doi.org/10.1109/TITS.2004.825086
  23. T. Naito, T. Tsukada, K. Yamada, K. Kozuka, and S. Yamamoto, “Robust license plate recognition method for passing vehicles under outside environment,” IEEE Trans. Vehic. Techn. 49, 2309-2319 (2000). https://doi.org/10.1109/25.901900
  24. L.G. Minor and J. Sklansky, “The detection and segmentation of blobs in infrared images,” IEEE Trans. Sys. Man and Cybern. SMC-11, 194-201 (1981).

Cited by

  1. Best Combination of Binarization Methods for License Plate Character Segmentation vol.35, pp.3, 2013, https://doi.org/10.4218/etrij.13.0112.0545
  2. Region-based Corner Detection by Radial Projection vol.15, pp.2, 2011, https://doi.org/10.3807/JOSK.2011.15.2.152
  3. Robust Aircraft Detection with a Simple and Efficient Model vol.9, pp.4, 2018, https://doi.org/10.3390/info9040074