ANNxDAR biblio: Multiple Clasifiers

Artificial Neural Networks for Document Analysis and Recognition

Bibliography on Multiple Clasifiers

[ALOK01] M. Aksela, J. Laaksonen, E. Oja, and J. Kangas. Rejection methods for an adaptive committee classifier. In ICDAR 01, pages 982-986, 2001.

[AS99] A.S. Atukorale and P.N. Suganthan. Combining classifiers based on confidence values. In ICDAR 99, pages 37-40, 1999.

[CdC00] S.E.N. Correia and J.M. de Carvalho. Optimizing the recognition rates of unconstrained handwritten numerals using biorthogonal spline wavelets. In ICPR 00, volume 2, pages 251-254, 2000.

[CDTV92] L.P. Cordella, C. De Stefano, F. Tortorella, and M. Vento. Improving character recognition rate by a multi-net neural classifier. In ICPR 92, pages 615-618, 1992.

[CT94] Y-S. Chen and W-H. Tsai. Preclassification of Chinese characters by hierarchical neural networks. In International Workshop on Frontiers in Handwriting Recognition, pages 464-471, 1994.

[DKS01] J.X. Dong, A. Krzyzak, and C.Y. Suen. A multi-net local learning framework for pattern recognition. In ICDAR 01, pages 328-332, 2001.

[EN97] R. Eigenmann and J. A. Nossek. Modification of hard-limiting multilayer neural networks for confidence evaluation. In ICDAR 97, pages 1087-1091, 1997.

[ERL03] A. Ennaji, A. Ribert, and Y. Lecourtier. From data topology to a modular classifier. Int'l J. Doc. Anal. Rec., 6(1):1-9, 2003.

[FCXP00] H.-C. Fu, H.-Y. Chang, Y.Y. Xu, and H.-T. Pao. User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks. IEEE TNN, 11(6):1373-1384, 2000.

[FGMS01] E. Francesconi, M. Gori, S. Marinai, and G. Soda. A serial combination of connectionist-based classifiers for OCR. Int'l J. Doc. Anal. Rec., 3(3):160-168, 2001.

[GMS93] M. Gori, S. Marinai, and G. Soda. Handwritten character recognition using the campionship algorithm. In 1st Italian Workshop Digital Image Proc. by Neural Net., pages 49-54, Rome, 1993.

[HKS94] Y.S. Huang, K.Liu, and C.Y. Suen. A neural network approach for multi-classifier recognition systems. In International Workshop on Frontiers in Handwriting Recognition, pages 235-244, 1994.

[HLS95] Y.S. Huang, K. Liu, and C.Y. Suen. The combination of multiple classifiers by a neural network approach. IJPRAI, 9(3):579, 1995.

[KD95] S.-H. Kim and J.-I. Doh. Off-line recognition of korean scripts using distance matching and neural classifiers. In ICDAR 95, pages 34-37, 1995.

[LK94] S. W. Lee and Y. J. Kim. Off-line recognition of totally unconstrained handwritten numerals using multiayer cluster neural network. In ICPR 94, pages 507-509, 1994.

[LK95] S.-W. Lee and Y.-J. Kim. Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. In ICDAR 95, pages 1010-1013, 1995.

[LS95] D.-S. Lee and S. N. Srihari. A theory of classifier combination: The neural network approach. In ICDAR 95, pages 42-45, 1995.

[MMF95] J. Mao, K. Mohiuddin, and T. Fujisaki. A two-stage multi-network OCR system with a soft pre-classifier and a network selector. In ICDAR 95, pages 78-81, 1995.

[OLS01] I.S. Oh, J.-S. Lee, and C.Y. Suen. A class-modularity for character recognition. In ICDAR 01, pages 64-68, 2001.

[OSBS03] L.S. Oliveira, R. Sabourin, F. Bortolozzi, and C.Y. Suen. Impacts of verification on a numeral string recognition system. PRL, 24:1023-1031, 2003.

[PJL93] J. Paik, S. Jung, and Y. Lee. Multiple combined recognition system for automatic processing of credit card slip applications. In ICDAR 93, pages 520-523, 1993.

[PL02] Z. Ping and C. Lihui. A novel feature extraction method and hybrid tree classification for handwritten numeral recognition. PRL, 23:45-56, 2002.

[PSH⁺93] B. Plessis, A. Sicsu, L. Heutte, E. Menu, E. Lecolinet, O. Debon, and J-V. Moreau. A multi-classifier combination strategy the recognition of handwritten cursive words. In ICDAR 93, pages 642-645, 1993.

[RHF01] A.F.R. Rahman, W.G.J. Howells, and M.C. Fairhust. A multiexpert framework for character recognition: a novel application of clifford networks. IEEE TNN, 12(1):101-112, 2001.

[SVL93] F. Fogelman Soulie, E. Viennet, and B. Lamy. Multi-modular Neural Network architectures : Applications in optical character and human face recognition. IJPRAI, 7(4):721 - 756, August 1993.

[TG93] H. Takahashi and T. D. Griffin. Recognition enhancement by linear tournament verification. In ICDAR 93, pages 585-588, 1993.

[VGC01] B. Verma, P. Gader, and W. Chen. Fusion of multiple handwritten recognition techniques. PRL, 22:991-998, 2001.

[WY00] J. Wang and H. Yan. A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural gas classifier. PRL, 21(6-7):625-635, 2000.

[XKS92] 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, 22(3):418-435, 1992.

[XWD00] B.H. Xiao, C.H. Wang, and R. W. Dai. Adaptive combination of classifiers and its application to handwritten chinese character recognition. In ICPR 00, pages 327-330, 2000.

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[ALOK01]	M. Aksela, J. Laaksonen, E. Oja, and J. Kangas. Rejection methods for an adaptive committee classifier. In ICDAR 01, pages 982-986, 2001.
[AS99]	A.S. Atukorale and P.N. Suganthan. Combining classifiers based on confidence values. In ICDAR 99, pages 37-40, 1999.
[CdC00]	S.E.N. Correia and J.M. de Carvalho. Optimizing the recognition rates of unconstrained handwritten numerals using biorthogonal spline wavelets. In ICPR 00, volume 2, pages 251-254, 2000.
[CDTV92]	L.P. Cordella, C. De Stefano, F. Tortorella, and M. Vento. Improving character recognition rate by a multi-net neural classifier. In ICPR 92, pages 615-618, 1992.
[CT94]	Y-S. Chen and W-H. Tsai. Preclassification of Chinese characters by hierarchical neural networks. In International Workshop on Frontiers in Handwriting Recognition, pages 464-471, 1994.
[DKS01]	J.X. Dong, A. Krzyzak, and C.Y. Suen. A multi-net local learning framework for pattern recognition. In ICDAR 01, pages 328-332, 2001.
[EN97]	R. Eigenmann and J. A. Nossek. Modification of hard-limiting multilayer neural networks for confidence evaluation. In ICDAR 97, pages 1087-1091, 1997.
[ERL03]	A. Ennaji, A. Ribert, and Y. Lecourtier. From data topology to a modular classifier. Int'l J. Doc. Anal. Rec., 6(1):1-9, 2003.
[FCXP00]	H.-C. Fu, H.-Y. Chang, Y.Y. Xu, and H.-T. Pao. User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks. IEEE TNN, 11(6):1373-1384, 2000.
[FGMS01]	E. Francesconi, M. Gori, S. Marinai, and G. Soda. A serial combination of connectionist-based classifiers for OCR. Int'l J. Doc. Anal. Rec., 3(3):160-168, 2001.
[GMS93]	M. Gori, S. Marinai, and G. Soda. Handwritten character recognition using the campionship algorithm. In 1st Italian Workshop Digital Image Proc. by Neural Net., pages 49-54, Rome, 1993.
[HKS94]	Y.S. Huang, K.Liu, and C.Y. Suen. A neural network approach for multi-classifier recognition systems. In International Workshop on Frontiers in Handwriting Recognition, pages 235-244, 1994.
[HLS95]	Y.S. Huang, K. Liu, and C.Y. Suen. The combination of multiple classifiers by a neural network approach. IJPRAI, 9(3):579, 1995.
[KD95]	S.-H. Kim and J.-I. Doh. Off-line recognition of korean scripts using distance matching and neural classifiers. In ICDAR 95, pages 34-37, 1995.
[LK94]	S. W. Lee and Y. J. Kim. Off-line recognition of totally unconstrained handwritten numerals using multiayer cluster neural network. In ICPR 94, pages 507-509, 1994.
[LK95]	S.-W. Lee and Y.-J. Kim. Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network. In ICDAR 95, pages 1010-1013, 1995.
[LS95]	D.-S. Lee and S. N. Srihari. A theory of classifier combination: The neural network approach. In ICDAR 95, pages 42-45, 1995.
[MMF95]	J. Mao, K. Mohiuddin, and T. Fujisaki. A two-stage multi-network OCR system with a soft pre-classifier and a network selector. In ICDAR 95, pages 78-81, 1995.
[OLS01]	I.S. Oh, J.-S. Lee, and C.Y. Suen. A class-modularity for character recognition. In ICDAR 01, pages 64-68, 2001.
[OSBS03]	L.S. Oliveira, R. Sabourin, F. Bortolozzi, and C.Y. Suen. Impacts of verification on a numeral string recognition system. PRL, 24:1023-1031, 2003.
[PJL93]	J. Paik, S. Jung, and Y. Lee. Multiple combined recognition system for automatic processing of credit card slip applications. In ICDAR 93, pages 520-523, 1993.
[PL02]	Z. Ping and C. Lihui. A novel feature extraction method and hybrid tree classification for handwritten numeral recognition. PRL, 23:45-56, 2002.
[PSH⁺93]	B. Plessis, A. Sicsu, L. Heutte, E. Menu, E. Lecolinet, O. Debon, and J-V. Moreau. A multi-classifier combination strategy the recognition of handwritten cursive words. In ICDAR 93, pages 642-645, 1993.
[RHF01]	A.F.R. Rahman, W.G.J. Howells, and M.C. Fairhust. A multiexpert framework for character recognition: a novel application of clifford networks. IEEE TNN, 12(1):101-112, 2001.
[SVL93]	F. Fogelman Soulie, E. Viennet, and B. Lamy. Multi-modular Neural Network architectures : Applications in optical character and human face recognition. IJPRAI, 7(4):721 - 756, August 1993.
[TG93]	H. Takahashi and T. D. Griffin. Recognition enhancement by linear tournament verification. In ICDAR 93, pages 585-588, 1993.
[VGC01]	B. Verma, P. Gader, and W. Chen. Fusion of multiple handwritten recognition techniques. PRL, 22:991-998, 2001.
[WY00]	J. Wang and H. Yan. A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural gas classifier. PRL, 21(6-7):625-635, 2000.
[XKS92]	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, 22(3):418-435, 1992.
[XWD00]	B.H. Xiao, C.H. Wang, and R. W. Dai. Adaptive combination of classifiers and its application to handwritten chinese character recognition. In ICPR 00, pages 327-330, 2000.