DOI QR코드

DOI QR Code

Comparative Performance Evaluation of Binarization Methods for Vehicle License Plate

자동차 번호판 이진화 방법에 대한 성능 비교

  • Published : 2009.08.28

Abstract

License plate recognition is an active research area. but few comparative studies on license plate binarization have been conducted. Many related researchers have experienced similar trial and error for finding an effective binarization method. To reduce this trial and error, this study implemented some binarization methods and quantitatively compared the performance of the methods. The performance evaluation consists of a low level measure and a high level measure, so it can evaluate not only the quality of binarized image itself but also the usefulness of the result. The performance evaluation was separately performed with three groups of images so as to understand the properties of the binarization methods. Experimental results show that the quality of binarization is more dependent on the evenness of illumination than the intensity of illumination. The Otsu's method has acquired the most effective performance in the group of even illumination images and the Niblack's method with parameter correction has shown the best quality in the group of uneven illumination images.

자동차 번호 인식에 대한 연구가 활발히 이루어져 왔으나, 번호판 이진화 방법들에 대한 비교 연구는 거의 이루어지지 않았다. 이로 인하여 관련 연구자들마다 효과적인 이진화 방법을 찾기 위하여 유사한 시행착오를 겪어 왔다. 본 연구에서는 이러한 시행착오를 줄일 수 있도록 기존의 번호판 이진화 방법들을 구현하여 성능을 양적으로 비교 제시하였다. 이진화 성능 측정은 저수준 척도와 고수준 척도를 모두 사용함으로써 이진화 자체에 대한 평가뿐만 아니라 후속 단계에서의 유용성을 함께 고려하였다. 그리고 이진화 방법들의 특성을 파악하기 위하여 조도의 특성에 따라 번호판 영상을 세 그룹으로 분류하여 이진화 성능을 측정하였다. 실험 결과 조도의 강도보다는 조도의 균일성 여부가 이진화 성능에 더 큰 영향을 미치는 것으로 나타났다. 조도가 균열한 영상은 Otsu의 방법이 가장 효과적이었으며, 조도가 불균일한 영상은 파라미터를 보정한 Niblack 방법이 가장 좋은 결과를 나타냈다.

Keywords

References

  1. N. Otsu, "A threshold selection method fromgray-level histograms," IEEE Trans. on SMC, Vol.9, No.1, pp.62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  2. V. Shapiro, G. Gluhchev, and D. Dimov,"Towards a multinational car license platerecognition system," Machine Vision and Applications, Vol.17, pp.173-183, 2006. https://doi.org/10.1007/s00138-006-0023-5
  3. 안영준, 위규범, 홍만표, "중국 자동차 번호판 인식", 정보처리학회논문지, 제14-B권, 제2호, pp.81-88, 2007. https://doi.org/10.3745/KIPSTB.2007.14-B.2.081
  4. F. Yang, Z. Ma, and M. Xie, "A Novel Binarization Approach for License Plate," Procs. of Industrial Electronics and Applications, pp.1-4, 2006. https://doi.org/10.1109/ICIEA.2006.257232
  5. Y.-Q. Yang, J. Bai, R.-L. Tian, and N. Liu, "A Vehicle License Plate Recognition System Based on Fixed Color Collocation," Proc. of the 4th International Conf. on Machine Learning and Cybernetics, pp.5394-5397, 2005. https://doi.org/10.1109/ICMLC.2005.1527897
  6. W. Niblack, An Introduction to Digital Image Processing, pp.115-116, Englewood Cliffs,N.J.:Prentice Halll, 1986.
  7. J. Bernsen, "Dynamic thresholding of grey-level images," Proc. of ICPR, pp.1251-1255, 1986.
  8. X.-Y. Yang, K.-L. Kim, and B.-K. Hwang, "An Efficient Binarization Method for Vehicle License Plate Character Recognition," Journal of Korea Multimedia Society, Vol.11, No.12, pp.1649-1657, 2008.
  9. C. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas, "A License Plate-Recognition Algorithm for Intelligent Transportation System Applications," IEEE Trans. on Intelligent Transportation Systems,Vol.7, No.3, pp.377-392, 2006. https://doi.org/10.1109/TITS.2006.880641
  10. J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recognition, Vol.33, pp.225-236, 2000. https://doi.org/10.1016/S0031-3203(99)00055-2
  11. J.-M. Guo and Y.-F. Liu, "License Plate Localization and Character Segmentation with Feedback Self-Learning and Hybrid Binarization Techniques," IEEE Trans. on Vehicular Technology, Vol.57, No.3,pp.1417-1424, 2008. https://doi.org/10.1109/TVT.2007.909284
  12. B.-F. Wu, S.-P. Lin and C.-C. Chiu,"Extracting characters from real vehicle license plates out-of-doors," IET Comput. Vis., Vol.1, No.1, pp.2-10, 2007. https://doi.org/10.1049/iet-cvi:20050132
  13. M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic Imaging, Vol.13, No.1, pp.146-165, 2004. https://doi.org/10.1117/1.1631315
  14. C.-I. Chang, Y. Du, J. Wang, S.-M. Guo and P.D. Thouin, "Survey and comparative analysis of entropy and relative entropy thresholding techniqeus," IEE Proc. of Vis. Image Signal Process, Vol.153, No.6, pp.837-850, 2006. https://doi.org/10.1049/ip-vis:20050032
  15. P. Stathis, E. Kavallieratou, and N. Papamarkos, "An Evaluation Technique for Binarization Algorithms," Journal of Universal Computer Science, Vol.14, No.18, pp.3011-3030, 2008.
  16. O. D. Trier and A. K. Jain, "Goal-Directed Evaluation of Binarization Methods," IEEE Trans. on PAMI, Vol.17, No.12, pp.1191-1201, 1995. https://doi.org/10.1109/34.476511
  17. P. Stathis, E. Kavallieratou, and N. Papamarkos, "An Evaluation Survey of Binarization Algorithms on Historical Documents," Proc. of ICPR, pp.1-4, 2008.
  18. 이응주, 이수현, 김성진, "투영면 컨벌루션과 결정트리를 이용한 상태 적응적 차량번호판 인식시스템", 멀티미디어학회논문지, 제8권, 제11호, pp.1496-1509, 2005.
  19. Y. J. Zhang, "A Survey on Evaluation Methods for Image Segmentation," Pattern Recognition, Vol.29, No.8, pp.1335-1346, 1996. https://doi.org/10.1016/0031-3203(95)00169-7

Cited by

  1. A Binarization Technique using Histogram Matching for License Plate with a Shadow vol.19, pp.1, 2014, https://doi.org/10.5909/JBE.2014.19.1.56