Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Passport Recognition using Fuzzy Binarization and Enhanced Fuzzy RBF Network

  • Published : 2004.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

Today, an automatic and accurate processing using computer is essential because of the rapid increase of travelers. The determination of forged passports plays an important role in the immigration control system. Hence, as the preprocessing phase for the determination of forged passports, this paper proposes a novel method for the recognition of passports based on the fuzzy binarization and the fuzzy RBF network. First, for the extraction of individual codes for recognizing, this paper targets code sequence blocks including individual codes by applying Sobel masking, horizontal smearing and a contour tracking algorithm on the passport image. Then the proposed method binarizes the extracted blocks using fuzzy binarization based on the trapezoid type membership function. Then, as the last step, individual codes are recovered and extracted from the binarized areas by applying CDM masking and vertical smearing. This paper also proposes an enhanced fuzzy RBF network that adapts the enhanced fuzzy ART network for the middle layer. This network is applied to the recognition of individual codes. The results of the experiments for performance evaluation on the real passport images showed that the proposed method has the better performance compared with other approaches.

Keywords

References

  1. Jain, A. K. Fundamental of Digital Image Processing, Englewood Cliffs, New Jersey: Prentice-Hall, 1989.
  2. K. Y. Wang, R. G. Casey and F. M. J. Wahl, “Document analysis system,” IBM J. Res. Develop.26, No.6, pp.647-656, 1982 https://doi.org/10.1147/rd.266.0647
  3. K. B. Kim, S. W. Jang and C. K. Kim,”Recognition of Car License Plate by Using Dynamical Thresholding Method and Enhanced Neural Networks,” Lecture Notes in Computer Science, LNCS 2756, pp. 309-319, 2003. https://doi.org/10.1007/978-3-540-45179-2_39
  4. R. C. Gonzalez and P. Wintz, Digital Image Processing, Addison-Wesley Publishing Company Inc., 1977.
  5. G. A. Carpenter, S. Grossberg, Neural Networks for Vision and Image Processing, Massachusetts Institute of Technology, 1992.
  6. K. B. Kim, “An Enhanced Fuzzy ART Algorithm for The Effective Identifier Recognition From Shipping Container Image,” Journal of The Korean Instistitute of Communication Sciences, Vol.28, No.5C, pp.486-492. 2003.
  7. M. L. Kothari, S. Madnani and R. Segal, "Orthogonal Least Square Learning Algorithm Based Radial Basis Function Network Adaptive Power System Stabilizer," Proceedings of IEEE SMC, Vol. 1, pp.542-547, 1997.
  8. K. B. Kim, M. H. Kang and E. Y. Cha, "A Fuzzy Self Organized Backpropagation using Nervous System," Proceedings of IEEE SMC, Vol.5, pp.1457-1462, 1997
  9. K. B. Kim, H. W. Yun, "A Study on Recognition of Bronchgenic Cancer Cell using Fuzzy Neural Networks," Journal of Korean Society of Medical Informatics, Vol. 5, No. 1, pp.77-87, 1999.