Fusion of n-Tuple Based Classifiers for High Performance Handwritten Character Recognition
In this paper we propose a novel system for handwritten character recognition which exploits the representational power of n-tuple based classifiers while addressing successfully the issues of extensive memory size requirements usually associated with them. To achieve this we develop a scheme based on the ideas of multiple classifier fusion in which the constituent classifiers are simplified versions of the highly successful scanning n-tuple classifier. In order to explore the behaviour and statistical properties of our architecture we perform a series of cross-validation experiments drawn from the field of handwritten character recognition. The paper concludes with a number of comparisons with results on the same data set achieved by a diverse set of classifiers. Our findings clearly demonstrate the significant gains that can be obtained, simultaneously in performance and memory space reduction, by the proposed system.
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- F. M. Alkoot and J. Kittler. Improving product by moderating k-nn classifiers. In J. Kittler and F. Roli, editors, Second International Workshop on Multiple Classifier Systems, pages 429–439. Springer, 2001.Google Scholar
- M. C. Fairhurst and M. S. Hoque. Moving window classifier: Approach to off-line image recognition. Electronics Letters, 36(7):628–630, March 2000.Google Scholar
- H. Freeman. Computer processing of line-drawing images. ACM Computing Surveys, 6(1):57–98, March 1974.Google Scholar
- M. S. Hoque and M. C. Fairhurst. Face recognition using the moving window classifier. In Proceedings of 11th British Machine Vision Conference (BMVC2000), volume 1, pages 312–321, Bristol, UK, September 2000.Google Scholar
- M. S. Hoque and M. C. Fairhurst. A moving window classifier for off-line char-acter recognition. In Proceedings of 7th International Workshop on Frontiers in Handwriting Recognition, pages 595–600, Amsterdam, The Netherlands, September 2000.Google Scholar
- S. Lucas and A. Amiri. Recognition of chain-coded handwritten character images with scanning n-tuple method. Electronic Letters, 31(24):2088–2089, November 1995.Google Scholar
- A. F. R. Rahman and M. C. Fairhurst. Machine-printed character recognition revisited: Re-application of recent advances in handwritten character recognition research. Special Issue on Document Image Processing and Multimedia Environments, Image & Vision Computing, 16(12–13):819–842, 1998.Google Scholar
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation, in Parallel Distributed Processing, volume 1, pages 318–362. MIT Press, Cambridge, MA, 1986. D. E. Rumelhart and J. L. McClelland(Eds.).Google Scholar
- K. Sirlantzis, M. C. Fairhurst, and M. S. Hoque. Genetic Algorithms for Multiple Classifier System Configuration: A Case Study in Character Recognition, volume 2096 of LNCS, pages 99–108. Springer, 2001.Google Scholar