Journal of Security Engineering (보안공학연구논문지)

Innovation in Secure and Intelligent Engineering Systems (보안공학연구지원센터)
권호 표지
DOI QR코드

DOI QR Code

Enhancement of Car License Plate Recognition Rate and Security with Rotation Algorithm

회전 알고리즘을 이용한 자동차 번호판 문자 인식률 향상 및 문자 보안 강화

  • Oh, Hyuna (Dept. Computer Science, SangMyung Univ.) ;
  • Rhee, Eugene (Dept. Computer Science, SangMyung Univ.)
  • Received : 2016.01.18
  • Accepted : 2016.04.01
  • Published : 2016.04.30
⟨ Previous Next ⟩

Abstract

To increase the recognition rate of numbers and Hangul characters in tilted car license plates images, this paper suggests to rotate the images tilted more than 5 degrees. First, the input RGB image is revised by the white image and converted to the Gray scale image. After setting the background and text colors as 0(black) and the color of plates as 255(white) with this Gray scale image, the image is binary coded. And then, the coordinates of the vertices for each rotation direction of the binary coded image are obtained. Finally, using arc tangent, this rotation algorithm calculates the slanting angle with the obtained coordinates of the vertices and rotates the licence plate image with rotate sauce. This Rotation algorithm can be adopted to various character or number recognitions and give higher recognition rate than existing recognition system.

본 논문은 기울기가 5도 이상 기울어진 자동차 번호판 영상의 숫자 및 한글 인식률을 높이고자 번호판 영상을 회전시키려 한다. 먼저, 입력 영상은 자동차 번호판 RGB 색상의 영상으로 이 RGB 영상을 흰색 계열로 보정한 뒤 Gray scale 영상으로 바꾼다. 바뀐 Gray scale 영상을 이용하여 배경과 글자색은 0(검정색)으로 지정하며 번호판 색상은 255(하얀색)로 지정한 뒤 영상을 이진화 한다. 이진화 된 영상의 영상 회전 방향에 따라 해당하는 꼭짓점의 좌표를 구한다. 마지막으로 아크탄젠트를 이용하여 구해진 꼭짓점의 좌표로 기울어진 각도를 구한 뒤 회전 소스를 이용하여 각도만큼 번호판 영상을 회전시킨다. 회전 된 번호판 영상은 각종 문자 인식이나 숫자 인식에 이용되며 회전 알고리즘을 이용하면 이용하지 않았을 때보다 문자 혹은 숫자에 대한 인식률이 높아짐을 확인하였다.

Keywords

Acknowledgement

Supported by : 상명대학교

References

  1. P. Comelli, P. Ferragina, M. Notturno Granieri, and F. Stabile, Optical Recognition of Motor Vehicle License Plates, IEEE Trans. Vehicular Technology. (1994), Vol.44, No.4, pp.790-799.
  2. S. Wang and H. Lee, A Cascade Framework for a Real-Time Statistical Plate Recognition System, IEEE Trans. Information Forensics and Security. (2007), Vol.2, No.2, pp.267-282. https://doi.org/10.1109/TIFS.2007.897251
  3. B. Hongliang and L. Changping, Editors. A Hybrid License Plate Extraction Method Based on Edge Statistics and Morphology, Proceedings of the 17th International Conference on Pattern Recognition, (2004) August 23-26; Cambridge, England.
  4. D. Zheng, Y. Zhao, and J. Wang, An Efficient Method of License Plate Location, Pattern Recognition Letter. (2005), Vol.26, No.15, pp.2431-2438. https://doi.org/10.1016/j.patrec.2005.04.014
  5. A. Ahmadyfard and V. Abolghasemi, Editors. Detecting License Plate using Texture and Color Information. Proceedings of International Symposium on Telecommunications, (2008) August 27-28; Tehran, Iran.
  6. L. Zheng, X. He, B. Samali, and L. Yang, Editors. Accuracy Enhancement for License Plate Recognition. Proceedings of IEEE the 10th International Conference on Computer and Information Technology, (2010) June 29-July 1; Bradford, America.
  7. A. Dengel, Editor, ANASTASIL: A System for Low-level and High-level Geometric Analysis of Printed Documents, Springer, Berlin (1992).
  8. D. Le, G. Thoma, and H. Wechsler, Automated Page Orientation and Skew Angle Detection for Binary Document Images, Pattern Recognition, (1994), Vol.27, No.10, pp.1325-1344. https://doi.org/10.1016/0031-3203(94)90068-X