Eliciting Domain Knowledge in Handwritten Digit Recognition
Pattern recognition methods for complex structured objects such as handwritten characters often have to deal with vast search spaces. Developed techniques, despite significant advancement in the last decade, still face some performance barriers. We believe that additional knowledge about the structure of patterns, elicited from humans perceptions, will help improve the recognition’s performance, especially when it comes to classify irregular, outlier cases. We propose a framework for the transfer of such knowledge from human experts and show how to incorporate it into the learning process of a recognition system using methods based on rough mereology. We also demonstrate how this knowledge acquisition can be conducted in an interactive manner, with a large dataset of handwritten digits as an example.
- 1.Komori, K., Kawatani, T., Ishii, K., Iida, Y.: A feature concentrated method for character recognition. In: Gilchrist, B. (ed.) Information Processing 1977, Proceedings of the International Federation for Information Processing Congress 1977, Toronto, Canada, August 8-12, pp. 29–34. North Holland, Amsterdam (1977)Google Scholar
- 2.Nguyen, T.T., Skowron, A.: Rough set approach to domain knowledge approximation. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGRC 2003. LNCS, vol. 2639, pp. 221–228. Springer, Heidelberg (2003)Google Scholar
- 3.Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In: International Conference on Pattern Recognition (ICPR 2002), pp. I:568–571 (2002)Google Scholar
- 5.Polkowski, L., Skowron, A.: Constructing rough mereological granules of classifying rules and classifying algorithms. In: Bouchon-Meunier, B., Rios-Gutierrez, J., Magdalena, L., Yager, R.R. (eds.) Technologies for Constructing Intelligent Systems I, pp. 57–70. Physica-Verlag, Heidelberg (2002)Google Scholar
- 6.Schalkoff, R.J.: Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons, Chichester (1992)Google Scholar