반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

신용카드 번호의 광학적 인식

Optical Recognition of Credit Card Numbers

  • 정민철 (상명대학교 공과대학 컴퓨터시스템공학과)
  • Jung, Min Chul (Dept. of Computer System Engineering, Sangmyung University)
  • 투고 : 2014.02.25
  • 심사 : 2014.03.20
  • 발행 : 2014.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes a new optical recognition method of credit card numbers. Firstly, the proposed method segments numbers from the input image of a credit card. It uses the significant differences of standard deviations between the foreground numbers and the background. Secondly, the method extracts gradient features from the segmented numbers. The gradient features are defined as four directions of grayscale pixels for 16 regions of an input number. Finally, it utilizes an artificial neural network classifier that uses an error back-propagation algorithm. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using real credit card images. The results show that the proposed algorithm is quite successful for most credit cards. However, the method fails in some credit cards with strong background patterns.

키워드

참고문헌

  1. G. Nagy and S. Seth, "Modern Optical Character Recognition," The Froehlich/Kent Encyclopedia of telecommunications, F. Froehlich and A. Kent, eds., Marcel Dekker, Inc., Vol. 11, pp. 473-531, 1996.
  2. "Standard Notices about Vehicle Registration Plates," Ministry of Construction and Transportation, No. 2005-334, 2005.
  3. Jaesuk Oh, Junsoo Lee, Jinbum Jung, Jungha Kim, "Speed Limit Signs of Road Environment Detection and Analysis for Unmanned Ground Vehicle," The Korean Society of Automotive Engineers, pp. 1419- 1423, 2012.
  4. Hochul Kwok, Hyunsik Bae, Daeyon Ko, Haejung Kim, Chajong Kim, "A Study on Image Processing for Business Card Recognition on Smartphones," Society of Korea Information Technology: Workshop on Information Technology for Big Data Era, pp. 445-449, 2012.
  5. Junsik Im, Soohyung Kim, Chilwoo Lee, Gyeesang Lee, Hyungjung Yang, Myungeun Lee, "Wine Label Character Recognition in Mobile Phone Images using a Lexicon-Driven Post-Processing," Journal on Korean Institute of Information Scientists and Engineers: Computing Practices and Letters, Vol. 16, No. 5, pp. 546-550, 2010.
  6. Hyunwoo Kang, "License Plate Recognition for Speed and Red Light Enforcement System Based on Character Circumscribed Quadrilateral," Journal on Society of Korea Information Technology, Vol. 9, No. 3, pp. 67-76, 2011.
  7. Sangeun Park, Gyungwon Kang, Youngwoo Choi, Jinhyung Kim, "Recognition of Cardslip Images Using Extraction of ROI and Image Enhancement," Journal on Korean Institute of Information Scientists and Engineers: 2000 Fall Conference, Vol. 27, No. 2, pp. 341-343, 2000.
  8. Heejoo Park, Jinho Park, Kidong Boo, "Character Recognition System for the Components Used in Industry by the Information of Their Images," Journal on The Korea Society of Digital Industry & Information Management, Vol. 5, No. 1, pp. 53-60, 2000.
  9. N. Otsu, "A Threshold Selection Method from Gray Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-9, No. 1, pp. 62-66, 1979.
  10. Minchul Jung, "Character Segmentation in a Grayscale Image using the Standard Deviation," Journal of the Semiconductor & Display Technology, Vol. 11, No. 2, pp. 27-31, 2012.
  11. Favata J. T., "A Multiple Feature/Resolution Approach to Handprinted Digit and Character Recognition," International Journal of Imaging Systems and Technology, Vol. 7, pp. 304-311, 1996. https://doi.org/10.1002/(SICI)1098-1098(199624)7:4<304::AID-IMA5>3.0.CO;2-C