Handwritten numeral recognition via fuzzy logic and local discriminating features
This paper describes a system to recognize disconnected handwritten numerals based on the concept of fuzzy logic and discriminating local features extracted from numeral images. Initially, the skeleton of an unknown numeral is obtained and decomposed into several segments called branches. The branches, due to their nature, present fuzzy characteristics in terms of their straightness and orientation. Precisely the three fuzzy sets were defined and used to classify branch segments into straight line segments, parts of circles and circles. The membership grade functions are built for character branches and their values are computed for the sequences of pattern branch features which represent numerals. A numeral image is classified to sequence of branch pattern features with the largest overall membership value. In the case of tie, some local topological features such as the number and the position of end points, intersection points and bend points, are used for the classification.
KeywordsFuzzy logic discriminating local features disconnected handwritten numerals and image processing.
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- [AB81]C. Arcelli and G. S. Di Baja. “A thinning Algorithm Based on Prominence Detection”. Pattern Recognition, Vol. 113, N° 3, pp. 225–235, 1981.Google Scholar
- [AU92]Al-yousefi, H. S. Udpa. “Recognition of Arabic Characters”. IEEE-Trans. Syst., Man and Cybernetics, Vol. 14, N° 8, 195, 1992.Google Scholar
- [BK88]G. Baptista and K. M. Kulkarni. “A High Accuracy Algorithm for Recognition of Handwritten Numerals”. Pattern Recognition, Vol. 21, n° 4, pp. 287–291, 1988.Google Scholar
- [FM82]K. Fukushima and S. Miyake. ”Neocognitron: A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position”. Pattern Recognition, Vol. 15, pp. 455–469,1982.Google Scholar
- [JR72]J. T. Tou and R. C. Gonzalez. “Recognition of Handwritten Characters by Topological Feature Extraction and Multilevel Categorization”. IEEE Trans Comput., vol. C-21, pp. 776–785, July 72.Google Scholar
- [PA75]T. Pavlidis and F. Ali. “Computer Recognition of Handwritten Numerals by Polygonal Approximations.” IEEE-Trans. Syst., Man, Cybernetics, Vol. SMC-5, No pp.610–614, November, 1975.Google Scholar
- [Ped90]W. Pedrycz. “Fuzzy Sets in Pattern Recognition: Methodology and Methods”. Pattern Recognition, Vol.23, N° 1/2, pp. 121–146,1990.Google Scholar
- [SB84]M. Shridar and A. Badreldin. “High Accuracy Character Recognition Algorithm Using Fourier and Topological Descriptors”. Pattern Recognition, Vol. 17, No5, pp.515–524,1984.Google Scholar
- [SC74]P Siy and C. S. Chen. “Fuzzy Logic for Handwritten Numeral Character Recognition”. IEEE-Trans. Systems, Man, and Cybernetics, November, 1974.Google Scholar
- [WW94]G. Wang and J. Wang. “A New Hierarchical Approach for Recognition of Unconstrained Handwritten Numerals”. IEEE Trans. Consumer Electronics, Vol. 40, N° 3, 165 August, 1994.Google Scholar