Abstract
In this paper, a multi-classifier combination scheme based on weighting individual classifier’s candidates is proposed. Four individual classifiers are constructed with four different feature extraction approaches. A confidence function is defined for the classifier integration through weighting the similarity of different individual classifier candidate outputs. Different integration methods are studied. Application of the multi-classifier to handwriting Chinese character recognition demonstrates that the recognition rate of the integrated system can be improved by 2% or so, showing the effectiveness of the proposed method.
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© 2000 Springer-Verlag Berlin Heidelberg
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Wang, M., Jin, L., Li, K., Yin, J., Huang, J. (2000). A New Multi-classifier Combination Scheme and Its Application in Handwriting Chinese Character Recognition. In: Tan, T., Shi, Y., Gao, W. (eds) Advances in Multimodal Interfaces — ICMI 2000. ICMI 2000. Lecture Notes in Computer Science, vol 1948. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40063-X_61
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DOI: https://doi.org/10.1007/3-540-40063-X_61
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