Bibliography on Optical Character Recognition (both printed and handwritten characters)

[AAsF96]	A. Amin, H. Al-sadoun, and S. Fischer. Hand-printed arabic character recognition system using an artificial network. Pattern Recognition, 29(4):663-675, 1996.
[AG98]	E. Alpaydin and F. Gurgen. Comparison of statistical and neural classifiers and their applications to optical character recognition and speech classification. In Neural Network Systems Techniques and Applications, volume 5, pages 61-88. Academic Press, 1998.
[AIDG95]	H.I. Avi-Itzhak, T.A. Diep, and H. Garland. High accuracy optical character recognition using neural networks with centroid dithering. IEEE TPAMI, 17(2):218-224, 1995.
[Alp89]	E. Alpaydin. Optical character recognition using Neural Networks. In First IEEE International Conference on Artificial Neural Networks, 1989.
[Alp96]	E. Alpaydin. Selective attention for handwritten digit recognition. In Proc. NIPS, pages 771-777, 1996.
[Ami97]	A. Amin. Off line Arabic character recognition- a survey. In ICDAR 97, pages 596-599, 1997.
[AS95]	R. Audouin and L. Shastri. Incremental character recognition with feature attribution. In ICDAR 95, pages 837-840, 1995.
[AW93]	A. Amin and W.H. Wilson. Hand-printed character recognition system using artificial neural networks. In ICDAR 93, pages 943-946, 1993.
[Bar92]	J. Barnden. Connectionist symbol processing. Neural Networks, 5(5):853-868, 1992.
[BCG+94]	J.L. Blue, G. T. Candela, P.J. Grother, R. Chellappa, and C.L. Wilson. Evaluation of pattern classifiers for fingerprint and ocr applications. Pattern Recognition, 27:485-, 1994.
[Ber93]	I.G. Bernard. Multilayer perceptron and uppercase handwritten character recognition. In ICDAR 93, pages 935-938, 1993.
[BGG03]	A. Bellili, M. Gilloux, and P. Gallinari. An MLP-SVM combination architecture for offline handwritten digit recognition. Int'l J. Doc. Anal. Rec., 5(4):244-252, 2003.
[BLB97]	L. Bottou, Y. LeCun, and Y. Bengio. Global training of document processing systems using graph transformer networks. In Computer Vision and Pattern Recognition, pages 489-494, 1997.
[BPP95]	J. Basak, N.R. Pal, and S.K. Pal. A connectionist system for learning and recognition of structures: Application to handwritten characters. Neural Networks, 8(4):643-657, 1995.
[Bre94]	T.M. Breuel. A system for the off-line recognition of handwritten text. In ICPR 94, pages 129-134, 1994.
[BVB03]	M. Blumenstein, B. Verma, and H. Baasli. A novel feature extraction technique for the recognition of segmented handwritten characters. In ICDAR 03, pages 137-141, 2003.
[CAB+93]	A.K. Chhabra, Z. An, D. Balik, G. Cerf, K. Loris, P. Sheppard, R. Smith, and B. Wittner. High-order statistically derived combinations of geometric features for handprinted character recognition. In ICDAR 93, pages 397-401, 1993.
[CAS97]	J. Cao, M. Ahmadi, and M. Shridhar. A hierarchical neural network architecture for handwritten numeral recognition. Pattern Recognition, (2):289-294, 1997.
[CBF03]	T. Caelli, W. F. Bischof, and M. Ferraro. A comparison of neural and graphical models for syntactic and structural pattern recognition. In M. Gori and S. Marinai, editors, Proc. 1st IAPR-TC3 Workshop ANNPR, pages 1-7. ISBN 88-7957-221-0, 2003.
[CBK03]	G. Y. Chen, T.D. Bui, and A. Krzyzak. Contour based handwritten numeral recognition using multiwavelets and neural networks. Pattern Recognition, 36:1597-1604, 2003.
[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.
[CDV95]	L.P. Cordella, C. De Stefano, and M. Vento. A neural network classifier for OCR using structural descriptions. MVA, 8(5):336-342, 1995.
[CFG95]	C. Calabro, P. Ferragina, and M.N. Granieri. Recognition of hand-written rotated digits by neural networks. MVA, 8(5):351-357, 1995.
[CFHS03]	M. Cannon, M. Fugate, D. R. Hush, and C. Scovel. Selecting a restoration technique to minimize OCR error. IEEE TNN, 14(3):478-490, 2003.
[CG97]	J-H. Chiang and P.D. Gader. Recognition of handprinted numerals in VISA card application form. MVA, 10(3):144-149, 1997.
[Cho97a]	S.-B. Cho. Neural-network classifiers for recognizing totally unconstrained handwritten numerals. IEEE TNN, 8(1):43-53, 1997.
[Cho97b]	S.-B. Cho. Self-organizing map with dynamical node splitting: application to handwritten digit recognition. Neural Computation, 9:1345-1374, 1997.
[CK92]	S.-B. Cho and J. H. Kim. Recognition of large-set printed Hangul (Korean script) by two-stage backpropagation neural classifier. Pattern Recognition, 25(11):1353-1360, 1992.
[CKKK91]	S.-B. Cho, J. O. Kwon, Y. B. Kwon, and J. H. Kim. NETeye: A Neural Network system for recognizing multi-font/multi-size Hangul (Korean script) documents. In ICDAR 91, pages 812-820, 1991.
[CSKA92]	J. Cao, M. Shridhar, F. Kimura, and M. Ahmadi. Statistical and neural classification of handwritten numerals: A comparative study. In ICPR 92, pages 643-646, 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.
[CT97]	H.P. Chiu and D.C. Tseng. Invariant handwritten Chinese character recognition using fuzzy min-max neural networks. PRL, 18(5):481-492, 1997.
[CWK94]	H.-D. Chang, J.-F. Wang, and S.-C. Kuo. A bayesian neural network for separating similar complex handwritten chinese characters. PRL, 15(4):403 - 408, 1994.
[CWY95]	Z. Chi, J. Wu, and H. Yan. Handwritten numeral recognition using self-organizing maps and fuzzy rules. Pattern Recognition, 28(1):59-66, 1995.
[CY95]	Z.R. Chi and H. Yan. Handwritten numeral recognition using a small number of fuzzy rules with optimized defuzzification parameters. Neural Networks, 8(5):821-827, 1995.
[CY97]	K. Chung and J. Yoon. Performance comparison of several feature selection methods based on node pruning in handwritten character recognition. In ICDAR 97, pages 11-15, 1997.
[dCF98]	A. de Carvalho and M.C. Fairhurst. Applying adaptive logic networks to character recognition. PRL, 19:469-474, 1998.
[DCG03]	G. Daqi, X. Chao, and N. Guiping. Combinative neural-network-based classifiers for optical handwritten character and letter recognition. In Int'l Joint Conference on Neural Networks, volume 3, pages 2232-2237, 2003.
[dDV91]	A. d'Acierno, C. De Stefano, and M. Vento. A structural character recognition method using neural networks. In ICDAR 91, pages 803-811, 1991.
[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.
[dOLB00]	L. E. S. de Oliveira, E. Lethelier, and F. Bartolozzi. A new segmentation approach for handwritten digits. In ICPR 00, pages 323-326, 2000.
[dOVdC00]	J.J. de Oliveira, L. R. Veloso, and J. M. de Carcalho. Interpolation/decimation scheme applied to size normalization of character images. In ICPR 00, pages 577-580, 2000.
[DSK03]	J.-X. Dong, C.Y. Suen, and A. Krzyzak. High accuracy handwritten chinese character recognition using support vector machine. In M. Gori and S. Marinai, editors, Proc. 1st IAPR-TC3 Workshop ANNPR, pages 39-45. ISBN 88-7957-221-0, 2003.
[DSS93]	H. Drucker, R. Schapire, and P. Simard. Boosting performance in Neural Network. IJPRAI, 7(4):705 - 720, August 1993.
[DVW98]	D. Dori, Y. Velkovitch, and L. Wenyin. Object-process based segmentation and recognition of ansi and iso standard dimensioning texts. pages 212-231. Springer Verlag, LNCS 1072, 1998.
[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.
[EY92]	T. Ejima and T. Yoshida. A new multi-layered neural network for character recognition. In From Pixels to Features III, pages 459-466. North Holland, 1992.
[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.
[FI93]	K. Fukushima and T. Imagawa. Recognition and segmentation of connected characters with selective attention. Neural Networks, 6(1):33-42, 1993.
[FM82]	K. Fukushima and S. Miyake. Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition, 15(6):455-469, 1982.
[Fuk87]	K. Fukushima. A neural network model for selective attention in visual pattern recognition and associative recall. Applied Optics, 26(23):4985-4992, 1987.
[Fuk88]	K. Fukushima. Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks, 1(2):119-130, 1988.
[Fuk93]	K. Fukushima. Connected character recognition with a neural network. In ICDAR 93, pages 240-243, 1993.
[Fuk01]	K. Fukushima. Neocognitron of a new version: handwritten digit recognition. In Int'l Joint Conference on Neural Networks, volume 2, pages 1498-1503, 2001.
[FW91]	K. Fukushima and N. Wake. Handwritten alphanumeric character recognition by the Neocognitron. IEEE TNN, 2(3):355-365, 1991.
[GK93]	P. D. Gader and M. A. Khabou. Automated feature generation for handwritten digit recognition by neural networks. In International Workshop on Frontiers in Handwriting Recognition, pages 21-30, 1993.
[GK95]	S. Gazula and M.R. Kabuka. Design of supervised classifiers using boolean neural networks. IEEE TPAMI, 17(12):1239-1246, 1995.
[GKN96]	J. Gonzalez, J. Kanai, and T.A. Nartker. Prediction of OCR accuracy using a neural network. In International workshop on Document Analysis Systems, pages 323-337, 1996.
[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.
[GNL+93]	A. Gupta, M.V. Nagendraprasad, A. Liu, P.S.P. Wang, and S. Ayyadurai. An integrated architecture for recognition of totally unconstrained handwritten numerals. IJPRAI, 7(4):757 - 774, August 1993.
[Gos91]	B. Gosselin. Boolean neural network applied to the recognition of typographic characters. In ICDAR 91, pages 426-434, 1991.
[Guy91]	I. Guyon. Applications of neural networks to character recognition. IJPRAI, 5:353-382, 1991.
[GVB+92]	I. Guyon, V. Vapnik, B. Boser, L. Bottou, and S.A. Solla. Structural risk minimization for character recognition. In Proc. NIPS, pages 471-479, San Mateo, CA, 1992.
[GVK02]	L. Gang, B. Verma, and S. Kulkami. Experimental analysis of neural network based feature extractors for cursive handwriting recognition. In Int'l Joint Conference on Neural Networks, volume 3, pages 2837-2842, 2002.
[GWB98]	M. D. Garris, C. L. Wilson, and J. L. Blue. Neural network-based systems for handprint OCR applications. IEEE Trans. Image Processing, 7(8):1097-1112, 1998.
[HB97]	Y.S. Hwang and S.Y. Bang. Recognition of unconstrained handwritten numerals by a radial basis function neural network classifier. PRL, 18(7):657-664, 1997.
[HDR97]	G.E. Hinton, P. Dayan, and M. Revow. Modeling the manifolds of images of handwritten digits. IEEE TNN, 8(1):65-74, 1997.
[HF92]	H.L. Higgins and D.M. Ford. Unsupervised learning of prototype allographs in cursive script recognition. In From Pixels to Features III, pages 61-74. North Holland, 1992.
[HK94]	B. Hussain and M.R. Kabuka. A novel feature recognition neural network and its application to character recognition. IEEE TPAMI, 16(1):98-106, 1994.
[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.
[Ho93]	T. K. Ho. Recognition of handwritten digits by combining independent learning vector quantization. In ICDAR 93, pages 818-821, 20-22 October 1993.
[HSI+96]	A. Hasegawa, K. Shibata, K. Itoh, Y. Ichioka, and K. Inamura. An adaptive neural network: Application to character recognition on x-ray films. Neural Networks, 9(1):121-127, 1996.
[HSV93]	J. Hoffman, J. Skrzypek, and J.J. Vidal. Cluster network for recognition of handwritten, cursive script characters. Neural Networks, 6(1):69-78, 1993.
[HUW+98]	Y. Hamamoto, S. Uchimura, M. Watanabe, T. Yasuda, Y. Mitani, and S. Tomita. A gabor filter-based method for recognizing handwritten numerals. Pattern Recognition, 31:395-400, 1998.
[ISIS92]	K. Iwata, Y. Suwa, Y. Ino, and N. Suzumura. Handwritten alphanumeric recognition by self-growing neural network CombNET-II. In Int'l Joint Conference on Neural Networks, pages 228-234, 1992.
[JGH+88]	L.D. Jackel, H.P. Graf, W. Hubbard, J.S. Denker, and D. Henderson. An application of Neural Net chips : Handwritten digit recognition. In IEEE International conference on Neural Networks, pages 107 - 115, San Diego, 1988.
[JKHK95]	J.-H. Jang, J-.H. Ku, K.-S. Hong, and J.-H. Kim. Two-stage recognition of freight train id number under outdoor environment. In ICDAR 95, pages 986-990, 1995.
[JSS99]	M.-C. Jung, Y.-C. Shin, and S. N. Srihari. Multifont classification using typographical attributes. In ICDAR 99, pages 353-356, 1999.
[Kaw93]	T. Kawatani. Handprinted numeral recognition with the learning quadratic discriminant function. In ICDAR 93, pages 14-17, 1993.
[KB00a]	G. Kaufmann and H. Bunke. Automated reading of cheque amounts. PAA, 3(2):132-141, 2000.
[KB00b]	D. Kim and S.-Y. Bang. A handwritten numeral character classification using tolerant rough set. IEEE TPAMI, 22(9):923-937, 2000.
[KBKL01]	E. Kussul, T. Baidyk, L. Kasatkina, and V. Lukovich. Rosenblatt perceptrons for handwritten digit recognition. In Int'l Joint Conference on Neural Networks, volume 2, pages 1516-1520, 2001.
[KDS90]	A. Krzyzak, W. Dai, and C.Y. Suen. Unconstrained handwritten character classification using modified backpropagation model. In International Workshop on Frontiers in Handwriting Recognition, pages 155-166, 1990.
[KKKL97]	H.J. Kim, S.K. Kim, Kim K, H, and T.K. Lee. An HMM based character recognition network using level building. Pattern Recognition, 30(3):491-502, March 1997.
[KL91]	E. J. Kim and Y. Lee. Handwritten Hangul recognition using a modified neocognitron. Neural Networks, 4(6):743-750, 1991.
[KLK95]	Y.-J. Kim, S.-W. Lee, and M.-W. Kim. Parallel harwdware implementation of handwritten character recognition system on wavefront processor architecture. In ICDAR 95, pages 715-718, 1995.
[KPD92]	S. Knerr, L. Personnaz, and G. Dreyfus. Handwritten digit recognition by neural networks with single-layer training. IEEE TNN, 3(6):962-968, 1992.
[KS93]	J.N. Kidder and D. Seligson. Fast recognition of noisy digits. Neural Computation, 5(6):885-892, 1993.
[KSN97]	K. Keeni, H. Shimodaira, and K. Nakayama. On distributed representation of output layer for recognizing japanes kana characters using neural networks. In ICDAR 97, pages 600-603, 1997.
[KSR01]	U.V. Kulkarni, T.R. Sontakke, and G.D. Randale. Fuzzy hyperline segment neural network for rotation invariant handwritten character recognition. In Int'l Joint Conference on Neural Networks, volume 4, pages 2918-2923, 2001.
[LBBH98]	Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278-2324, 1998.
[LBD+90a]	Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Handwritten digit recognition with a back-propagation network. In Proc. NIPS, pages 396-404, Denver, CO, 1990.
[LBD+90b]	Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, and H. S. Baird. Constrained neural network for unconstrained handwritten digit recognition. In International Workshop on Frontiers in Handwriting Recognition, pages 145-154, 1990.
[LBD+98]	Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, and L.D. Jackel. Handwritten digit recognition with a backpropagation network. In Proc. NIPS, pages 396-404, 1998.
[LC93]	S. Lee and Y. Choi. Recognition of unconstrained handwritten numerals based on dual cooperative neural network. In International Workshop on Frontiers in Handwriting Recognition, pages 1-10, 1993.
[LC98]	K.T. Lim and S.I. Chien. Neural network based feature space generation for multiple databases of handwritten numerals. In ICPR98, volume 1, pages 375-377, 1998.
[LCSS99]	Z. Lu, Z. Chi, W.-C. Siu, and P. Shi. A background-thinning-based approach for separating and recognizing connected handwritten digit strings. Pattern Recognition, 32(6):921-933, 1999.
[LDW97]	X. Lin, X. Ding, and Y. Wu. Handwritten numeral recognition using mfnn based multiexpert combination strategy. In ICDAR 97, pages 471-474, 1997.
[Lee91]	Y. Lee. Handwritten digit recognition using k neighbor, radial basis function, backpropagation neural networks. Neural Computation, 3:440-449, 1991.
[Lee95]	S.W. Lee. Multilayer cluster neural network for totally unconstrained handwritten numeral recognition. Neural Networks, 8(5):783-792, 1995.
[Lee96]	S. W. Lee. Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network. IEEE TPAMI, 18(6):648-652, 1996.
[Lem93]	B. Lemarié. Practical implementation of a radial basis function network for handwritten digit recognition. In ICDAR 93, pages 412-415, 1993.
[LG97]	L.L. Lee and N.R. Gomes. Disconnected handwritten numeral image recognition. In ICDAR 97, pages 467-470, 1997.
[LHH93]	H.Y. Liao, J.S. Huang, and S.T. Huang. Stroke-based handwritten Chinese character reognition using Neural Networks. PRL, 14(10):833 - 840, October 1993.
[LJBD89]	Y. LeCun, L. Jackel, B. Boser, and J. Denker. Handwritten digit recognition: Application of neural network chips and automatic learning. IEEE Communications Magazine, 27(11):41-46, November 1989.
[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.
[LK95a]	S.-W. Lee and J.-S. Kim. Multi-lingual, multi-font and multi-size large-set character recognition using self-organizing neural networks. In ICDAR 95, pages 28-33, 1995.
[LK95b]	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.
[LK95c]	S.-W. Lee and Y.-J. Kim. A new type of recurrent neural network for handwritten character recognition. In ICDAR 95, pages 38-41, 1995.
[LKK94]	S-W. Lee, Y.J. Kim, and M.W. Kim. Multilayer cluster neural network for off-line recognition of totally unconstrained handwritten numerals. In International Workshop on Frontiers in Handwriting Recognition, pages 115-124, 1994.
[LKMT96]	S. W. Lee, C. H. Kim, H. Ma, and Y. Y. Tang. Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network. Pattern Recognition, 29(12):1953-1962, 1996.
[LKsYB99]	J.-S. Lee, O.-J. Kwon, and s. Y. Bang. Highly accurate recognition of printed characters through an improved two-stage classificatoin method. Pattern Recognition, 31(12):1935-1946, December 1999.
[LLC98]	H.-M. Lee, C.-C. Lin, and J.-Lm Chen. A preclassification method for handwritten chinese character recognition via fuzzy rules and seart neural net. IJPRAI, 12(6):743-762, 1998.
[LLP96]	S. W. Lee, D.-J. Lee, and H.-S. Park. A new methodology for gray-scale character segmentation and recognition. IEEE TPAMI, 18(10):1045-1050, 1996.
[LO90]	M.C. Lee and W.J.B. Oldham. Font recognition by a neural network. IJMMS, 33:41-61, 1990.
[LP92]	S. Lee and J.C. Pan. Handwritten numeral recognition based on hierarchically self-organizing learning networks with spatio-temporal pattern representation. In Computer Vision and Pattern Recognition, pages 176-182, 1992.
[LP96]	S. Lee and J.C.J. Pan. Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatiotemporal feature representation. IEEE TNN, 7(2):455-474, 1996.
[LS93a]	S-W. Lee and H-H. Song. Optimal design of reference models using simulated annealing combined with an improved LVQ3. In ICDAR 93, pages 244-249, 1993.
[LS93b]	D. Lopresti and J. S. Sandberg. Certifiable optical character recognition. In ICDAR 93, pages 432-435, 1993.
[LS95]	D.-S. Lee and S. N. Srihari. Dynamic classifier combination using neural network. In Proc. SPIE - Doc. Rec. II, pages 26-37, 1995.
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[LTS+92]	T. Li, Y.Y. Tang, S.C. Suen, L.Y. Fang, and A.J. Jennings. A structurally adaptive neural tree for the recognition of large character set. In ICPR 92, pages 187-190, 1992.
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[MIW+98]	Y. Miyake, S. Inoue, T. Wakabayashi, F. Kimura, and S. Tsuruoka. Handwritten numerical recognition using autoassociative neural networks. In ICPR98, page PRP1, 1998.
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[MOS+02]	M. Morita, L.S. Oliveira, R. Sabourin, F. Bortolozzi, and C.Y. Suen. An HMM-MLP hybrid system to recognize handwritten dates. In Int'l Joint Conference on Neural Networks, volume 1, pages 867-872, 2002.
[MP91]	G. L. Martin and J. A. Pittman. Recognizing hand-printed letters and digits using backpropagation learning. Neural Computation, 3(2):258-267, 1991.
[MY93]	K. Miyahara and F. Yoda. Printed japanes character recognition based on multiple LVQ neural networks. In ICDAR 93, pages 250-253, 1993.
[OLS99]	I.-S. Oh, J.-S. Lee, and C. Y. Suen. Analysis of class separation and combination of class-dependent features for handwriting recognition. IEEE TPAMI, 21(10):1089-1094, 1999.
[OLS01]	I.S. Oh, J.-S. Lee, and C.Y. Suen. A class-modularity for character recognition. In ICDAR 01, pages 64-68, 2001.
[OS97]	I.-S. Oh and C. Y. Suen. A feature for character recognition based on directional distance distribution. In ICDAR 97, pages 288-292, 1997.
[OS02]	Il-S. Oh and C.Y. Suen. A class-modular feedforward neural network for handwriting recognition. Pattern Recognition, 35:229-244, 2002.
[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.
[PG00]	J. Park and V. Govindaraju. OCR in a hierarchical feature space. IEEE TPAMI, 22(4):400-4073, 2000.
[PL02]	Z. Ping and C. Lihui. A novel feature extraction method and hybrid tree classification for handwritten numeral recognition. PRL, 23:45-56, 2002.
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