Abstract
Recognition confidence plays an important role in the selection of rejection threshold and the combination of multiple classifiers. In this paper, we first present a systematic theory on classifier's confidence, which includes the definition, the concept of generalized confidence, optimal rejection theorem and the relationship between confidence value and recognition rate. Then we propose a method for the evaluation of recognition confidence. The theory and method are strongly supported by the practice in handwritten numeral recognition and off-line handwritten Chinese character recognition.
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References
Lin Xiaofan, et al, “Handwritten Numeral Recognition Using MFNN Based Adaptive Multiexpert Combination”, ICDAR'97, 1997.8
Michael D. Richard, et al, “Neural Network Classifiers Estimate Bayesian a posteriori Probabilities”, Neural Computation, pp461–483, 1991.3
T. M. Cover and P.E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Tans. Inform. Theory, vol.IT-13, pp. 21–27, 1967
Michael D. Garris, et. al, “NIST Form-Based Handprint Recognition System”,National Institute of Standards and Technology, 1994.6
Youbin Chen, et al, “New Method for the Extraction of Handwritten Chinese Characters”, submitted to Signal Processing
Stephen J. Smith, et al, “Handwritten Character Classification Using Nearest Neighbor in Large Database”, IEEE. Trans. Pattern Anal. Machine Intell, vol.16, no.9,pp915–919,1994.9
Guo Hong,et al, “Comprehensive Recognition Method for Improving the Robustness of Recognition of Printed Chinese Characters”, Proc. of the 1st Conf on Multimodal Interface, pp.221–226, 1996.10
F.F Soulie, E. Viennet, “Multi-Modular Neural Network Architectures: Applications in Optical Character and Human Face Recognition”, Advances in Pattern Recognition Systems Using Neural Network Technologies, pp77–111, 1993
Jinhui Liu, “Research on Contour Model Based Handwritten Chinese Character Recognition”,Ph.D Thesis, Tsinghua University, 1997
Teuvo Kohonen, “The Self-Organizing Map”, Proc. of IEEE, vol 78, no 9,1990.9
Xiaofan Lin, et al, “Theoretical Analysis of the Confidence Estimation for Nearest Neighbor Classfier”, Chinese Science Bulletin
Lei Xu, et al, “Methods of Combining Multiple Classifiers and Their Applications to Handwritten Recognition”, IEEE Trans. System, Man and Cybernetics, vol. 22, no 3, 1992.6
C.Y. Suen, et. Al, “Computer Recognition of Unconstrained Handwritten Numerals”, IEEE Trans. on Pattern Anal. And Machine Intell., vol.80, no.7, pp 1162–1180
Dar-Shyang Lee, “A Theory of Classifier Combination: The Neural Network Approach”, Ph.D Thesis, SUNY at Buffalo, 1995.4
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© 1997 Springer-Verlag Berlin Heidelberg
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Lin, X., Ding, X., Chen, Y., Liu, J., Wu, Y. (1997). Evaluation and application of recognition confidence in OCR. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_117
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DOI: https://doi.org/10.1007/3-540-63930-6_117
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