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A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
January 1995 (vol. 17 no. 1)
pp. 90-94
Abstract—For pattern recognition, when a single classifier cannot provide a decision which is 100 percent correct, multiple classifiers should be able to achieve higher accuracy. This is because group decisions are generally better than any individual’s. Based on this concept, a method called the “Behavior-Knowledge Space Method” was developed, which can aggregate the decisions obtained from individual classifiers and derive the best final decisions from the statistical point of view. Experiments on 46,451 samples of unconstrained handwritten numerals have shown that this method achieves very promising performances and outperforms voting, Bayesian, and Dempster-Shafer approaches.
[1] 90 S. Mori, C.Y. Suen, and K. Yamamoto, “Historical Review of OCR Research and Development,” Proc. IEEE, vol. 80, no. 7, pp. 1,029-1,058, 1992.[2] F. Kimura,Z. Chen,, and M. Shridhar., “An Integrated Character Recognition Algorithm for Locating and Recognizing Zip Codes,” United States Postal Service - Advanced Technology Conference, pp. 605-619, Nov. 1990.[3] R. Bradford and T. Nartker., “Error Correlation in Contemporary OCR Systems,” 1st Int. Conf. on Document Analysis and Recognition, pp. 516-524, 1991.[4] J. Franke and E. Mandler, "A Comparison of Two Approaches for Combining the Votes of Cooperating Classifiers," Proc. 11th IAPR Int'l Conf. Pattern Recognition, Conf. B: Pattern Recognition Methodology and Systems, vol. 2, pp. 611-614, 1992.[5] L. Xu, A. Krzyzak, and C.Y. Suen, “Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition,” IEEE Trans. Systems, Man, and Cybernetics, vol. 22, no. 3, pp. 418-435, 1992.[6] T.K. Ho, J.J. Hull, and S.N. Srihari, “Decision Combination in Multiple Classifiers Systems,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 1, pp. 66-75, Jan. 1994.[7] C. Suen, R. Legault, C. Nadal, M. Cheriet, and L. Lam, “Building a New Generation of Handwriting Recognition Systems,” Pattern Recognition Letters, vol. 14, pp. 303-315, 1993.[8] J. Pearl, Probabilistic Reasoning in Intelligent Systems. San Mateo, Calif.: Morgan Kaufman, 1988.[9] M.D. McLeish,P. Yao,, and T. Stirtzinger., “A Study on the Use of Belief Functions for Medical Expert Systems,” Journal of Applied Statistics, vol. 18, no. 1, pp. 155-174, 1991.[10] Y.S. Huang and C.Y. Suen., “An Optimal Method of Combining Multiple Experts for Handwritten Numerical Recognition,” Pre-Proc. International Workshop on Frontiers in Handwriting Recognition, pp. 11-20,Buffalo, New York, USA, 1993.[11] P.A. Devijver and J. Kittler., Pattern Recognition—A Statistical Approach,London: Prentice-Hall, 1982.[12] L.N. Kanal., “On Pattern, Categories, and Alternative Realities,” Pattern Recognition Letters, vol. 14, no. 3, pp. 241-255, 1993.
Index Terms:
Unconstrained Handwriting Recognition, Combination of Multiple Classifiers, Evidence Aggregation, Behavior-Knowledge Space, Knowledge Modeling.
Citation:
Y. S. Huang, C. Y. Suen, "A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 1, pp. 90-94, Jan. 1995, doi:10.1109/34.368145
