Browse > Article
http://dx.doi.org/10.5207/JIEIE.2003.17.4.026

Image Character Recognition using the Mellin Transform and BPEJTC  

서춘원 (김포대학 전자정보계열)
고성원 (김포대학 전자정보계열)
이병선 (김포대학 전자정보계열)
Publication Information
Journal of the Korean Institute of Illuminating and Electrical Installation Engineers / v.17, no.4, 2003 , pp. 26-35 More about this Journal
Abstract
For the recognizing system to be classified the same or different images in the nature the rotation, scale and transition invariant features is to be necessary. There are many investigations to get the feature for the recognition system and the log-polar transform which is to be get the invariant feature for the scale and rotation is used. In this paper, we suggested the character recognition methods which are used the centroid method and the log-polar transform with the interpolation to get invariant features for the character recognition system and obtained the results of the above 50% differential ratio for the character features. And we obtained the about 90% recognition ratio from the suggested character recognition system using the BPEJTC which is used the invariant feature from the Mellin transform method for the reference image. and can be recognized the scaled and rotated input character. Therefore, we suggested the image character recognition system using the Mellin transform method and the BPEJTC is possible to recognize with the invariant feature for rotation scale and transition.
Keywords
Mellin transform; character recognition; invariant feature; BPEJTC;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. Casasent, S. F. Xia, A. J. Lee and J. Z. Jung, “Real-time Deformation Invariant Optical Pattern Recognition using Coordinate Transformations”, Appl. Opt., vol.26, no.9, pp.938-942, 1987.   DOI
2 Saburo Tsuji, Michiharu Osada and Masahiko Yachida, “Tracking and Segmentation of Moving Objects in Dynamic Line Images”, IEEE vol.PAMI-2, no.6, pp.516-522, November, 1980.
3 E. S, Kim, S. Y. Yi, and J. H. Lee, “Real-Time Tracking system based on Joint Transform Correlator and Neural Network Algorithm”, Proc. SPIE, vol.1812, 1992.   DOI
4 Skolnick M.M., Brown, R.H., Bhagvati, C., Wolf, B.R.,“Morphological algorithms for centroid normalization in relational matching”, IEEE International Symposium on Circuits and Systems 1989, vol.2, 987-990, 1989.   DOI
5 Anqi Ye and David Casasent, “Morphological Wavelet Transform for Distortion- Invariant Object Detection in Clutter”, SPIE Wavelet Applications, vol.2242, pp.525-537, 1994.   DOI
6 D. J. Gregoris, S. K. W. Yu, and S. Tritchew, “Wavelet transform-based filtering for the enhancement of dim targets in FLIR images”, SPIE Wavelet Applications, vol. 2242, pp.573-583, 1994.   DOI
7 Jong-Kwon Won, Sang-Yi Yi, Chung-Sang Ryu, Seung-Hyun Lee, Eun-Soo Kim, “Moving Target Segmentation using the Sequential Correlation”, Proc. of KICS, vol.14, no.2, pp.947-950, 1995.
8 Y. N. Hsu and H. H. Arsenault, “Optical Pattern Recognition using the Circular Harmonic Expansion”, Appl. Opt., vol.21, pp.4016-4025, 1982.   DOI   ScienceOn
9 Ramesh C. Jain, “Segmentation of Frame Sequences Obtained by a Moving Observer”, IEEE vol.PAMI-6, no.5, pp.624-629, September, 1984.   DOI   ScienceOn
10 R. Jain, S. Bartlett, and N. O'Brien, “Some Experiments in Ego-motion Complex Logarithmic Mapping”, Advances in Computer Vision and Image Processing, vol.3, pp.145-177, 1988.
11 James H. Duncan and Tsai-Chia Chou, “On the Detection of Motion and the Computation of Optical Flow”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.14, no.3, pp.346-352, March, 1992.   DOI   ScienceOn