Classification and learning methods for character recognition: Advances and remaining problems

Studies in Computational Intelligence

Volumn 90, Issue , 2008, Pages 139-161

  Liu, Cheng Lin ^a   Fujisawa, Hiromichi ^b  

^a INSTITUTE OF AUTOMATION   (China)

^b HITACHI LTD   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38049031280     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-76280-5_6     Document Type: Review

References (80)

1. Mori, S., Suen, C.Y., Yamamoto, K.: Historical review of OCR research and development. Proc. IEEE 80(7) (1992) 1029-1058
2. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11) (1998) 2278-2324
3. Suen, C.Y., Liu, K., Strathy, N.W.: Sorting and recognizing cheques and financial documents. In: Lee SW, Nakano Y (eds) Document Analysis Systems: Theory and Practice. Springer, LNCS 1655 (1999) 173-187
4. Liu, C.L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: Benchmarking of state-of-the-art techniques. Pattern Recognition 36(10) (2003) 2271-2285
5. Marinai, S., Gori, M., Soda, G.: Artificial neural networks for document analysis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(1) (2005) 23-35
6. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1) (2000) 4-37
7. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, 2nd edition (1990)
8. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
9. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, 2nd edition (2001)
10. Friedman, J.H.: Regularized discriminant analysis. J. Am. Statist. Ass. 84(405) (1989) 165-175
11. Kimura, F., Takashina, K., Tsuruoka, S., Miyake, Y.: Modified quadratic discriminant functions and the application to Chinese character recognition. IEEE Trans. Pattern Anal. Mach. Intell. 9(1) (1987) 149-153
12. Kimura, F., Wakabayashi, T., Tsuruoka, S., Miyake, Y.: Improvement of handwritten Japanese character recognition using weighted direction code histogram. Pattern Recognition 30(8) (1997) 1329-1337
13. Ikeda, M., Tanaka, H., Motooka, T.: Projection distance method of recognition of handwritten characters. Trans. IPS Japan 24(1) (1983) 106-112
14. Nakajima, T., Wakabayashi, T., Kimura, F., Miyake, Y.: Accuracy improvement by compound discriminant functions for resembling character recognition. Trans. IEICE Japan J83-D-II(2) (2000) 623-633
15. Hinton, G.E., Dayan, P., Revow, M.: Modeling the manifolds of images of handwritten digits. IEEE Trans. Neural Networks 8(1) (1997) 65-74
16. Kim, H.C., Kim, D., Bang, S.Y.: A numeral character recognition using the PCA mixture model. Pattern Recognition Letters 23 (2002) 103-111
17. Tsay, M.K., Shyu, K.H., Chang, P.C.: Feature transformation with generalized LVQ for handwritten Chinese character recognition. IEICE Trans. Information and Systems E82-D(3) (1999) 687-92
18. Zhang, P., Bui, T., Suen, C.Y.: Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals. In: Proc. 8th ICDAR, Seoul, Korea, 1 (2005) 136-140
19. Kawatani, T., Shimizu, H.: Handwritten Kanji recognition with the LDA method. In: Proc. 14th ICPR, Brisbane, 2 (1998) 1031-1035
20. Wakabayashi, T., Shi, M., Ohyama, W., Kimura, F.: Accuracy improvement of handwritten numeral recognition by mirror image learning. In: Proc. 6th ICDAR, Seattle (2001) 338-343
21. Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Proc. 7th ICDAR, Edinburgh, UK, 2 (2003) 958-962
22. Oh, I.S., Suen, C.Y.: A class-modular feedforward neural network for handwriting recognition. Pattern Recognition 35(1) (2002) 229-244
23. Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, MA (1989)
24. Schürmann, J.: Pattern Classification: A Unified View of Statistical and Neural Approaches. Wiley Interscience (1996)
25. Kreßel, U., Schürmann, J.: Pattern classification techniques based on function approximation. In: Bunke H, Wang PSP (eds) Handbook of Character Recognition and Document Image Analysis, World Scientific (1997) 49-78
26. Franke, J.: Isolated handprinted digit recognition. In: Bunke H, Wang PSP (eds) Handbook of Character Recognition and Document Image Analysis, World Scientific (1997) 103-121
27. Liu, C.L., Sako, H.: Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition. Pattern Recognition 39(4) (2006) 669-681
28. Kimura, F., Inoue, S., Wakabayashi, T., Tsuruoka, S., Miyake, Y.: Handwritten numeral recognition using autoassociative neural networks. In: Proc. 14th ICPR, Brisbane, 1 (1998) 166-171
29. Zhang, B., Fu, M., Yang, H.: A nonlinear neural network model of mixture of local principal component analysis: Application to handwritten digits recognition. Pattern Recognition 34(2) (2001) 203-214
30. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9) (1990) 1464-1480
31. Liu, C.L., Nakagawa, M.: Evaluation of prototype learning algorithms for nearest neighbor classifier in application to handwritten character recognition. Pattern Recognition 34(3) (2001) 601-615
32. Liu, C.L., Sako, H., Fujisawa, H.: Discriminative learning quadratic discriminant function for handwriting recognition. IEEE Trans. Neural Networks 15(2) (2004) 430-444
33. Vapnik, V.: The Nature of Statistical Learning Theory. Springer-Verlag, New Work (1995)
34. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining 2(2) (1998) 1-43
35. Kressel, U.: Pairwise classification and support vector machines. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in Kernel Methods: Support Vector Learning, MIT Press (1999) 255-268
36. Bellili, A., Gilloux, M., Gallinari, P.: AnMLP-SVM combination architecture for offline handwritten digit recognition: Reduction of recognition errors by support vector machines rejection mechanisms. Int. J. Document Analysis and Recognition 5(4) (2003) 244-252
37. Dong, J.X., Krzyzak, A., Suen, C.Y.: An improved handwritten Chinese character recognition system using support vector machine. Pattern Recognition Letters 26(12) (2005) 1849-1856
38. Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. System Man Cybernet. 22(3) (1992) 418-435
39. Ho, T.K., Hull, J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16(1) (1994) 66-75
40. Rahman, A.F.R., Fairhurst, M.C.: Multiple classifier decision combination strategies for character recognition: A review. Int. J. Document Analysis and Recognition 5(4) (2003) 166-194
41. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3) (1998) 226-239
42. Duin, R.P.W.: The combining classifiers: To train or not to train. In: Proc. 16th ICPR, Quebec, Canada, 2 (2002) 765-770
43. Liu, C.L.: Classifier combination based on confidence transformation. Pattern Recognition, 38(1) (2005) 11-28
44. Suen, C.Y., Lam, L.: Multiple classifier combination methodologies for different output levels. In: Kittler J, Roli F (eds) Multiple Classifier Systems, Springer, LNCS 1857 (2000) 52-66
45. Breiman, L.: Bagging predictors. Machine Learning 24(2) (1996) 123-140
46. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Computer and System Sciences 55(1) (1997) 119-139
47. Ha, T., Bunke, H.: Off-line handwritten numeral recognition by perturbation method. IEEE Trans. Pattern Anal. Mach. Intell. 19(5) (1997) 535-539
48. Dahmen, J., Keysers, D., Ney, H.: Combined classification of handwritten digits using the virtual test sample method. In: Kittler J, Roli F (eds) Multiple Classifier Systems, Springer, LNCS 2096 (2001) 99-108
49. Tang, Y.Y., et al.: Offline recognition of Chinese handwriting by multifeature and multilevel classification. IEEE Trans. Pattern Anal. Mach. Intell. 20(5) (1998) 556-561
50. Wang, Q.R., Suen, C.Y.: Analysis and design of a decision tree based on entropy reduction and its application to large character set recognition. IEEE Trans. Pattern Anal. Mach. Intell. 6(4) (1984) 406-417
51. Suzuki, M., Omachi, S., Kato, N., Aso, H., Nemoto, Y.: A discrimination method of similar characters using compound Mahalanobis function. Trans. IEICE Japan J80-D-II(10) (1997) 2752-2760
52. Kato, N., Suzuki, M., Omachi, S., Aso, H., Nemoto, Y.: A handwritten character recognition system using directional element feature and asymmetric Mahalanobis distance. IEEE Trans. Pattern Anal. Mach. Intell. 21(3) (1999) 258-262
53. Fu, H.C., Xu, Y.Y.: Multilinguistic handwritten character recognition by Bayesian decision-based neural networks. IEEE Trans. Signal Processing 46(10) (1998) 2781-2789
54. Kimura, Y., Wakahara, T., Tomono, A.: Combination of statistical and neural classifiers for a high-accuracy recognition of large character sets. Trans. IEICE Japan J83-D-II(10) (2000) 1986-1994
55. Saruta, K., Kato, N., Abe, M., Nemoto, Y.: High accuracy recognition of ETL9B using exclusive learning neural network-II (ELNET-II). IEICE Trans. Information and Systems 79-D(5) (1996) 516-521
56. Fukumoto, T., Wakabayashi, T., Kumura, F., Miyake, Y.: Accuracy improvement of handwritten character recognition by GLVQ. In: Proc. 7th IWFHR, Amsterdam (2000) 271-280
57. Liu, C.L.: High accuracy handwritten Chinese character recognition using quadratic classifiers with discriminative feature extraction. In: Proc. 18th ICPR, Hong Kong, 2 (2006) 942-945
58. Liu, H., Ding, X.: Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes. In: Proc. 8th ICDAR, Seoul, Korea, 1 (2005) 19-23
59. Liu, H., Ding, X.: Handwritten Chinese character recognition based on mirror image learning and the compound Mahalanobis distance. J. Tsinghua Univ. (Sci & Tech) 46(7) (2006) 1239-1242 (in Chinese)
60. Liu, H., Ding, X.: Improve handwritten character recognition performance by heteroscedastic linear discriminant analysis, In: Proc. 18th ICPR, Hong Kong, 1 (2006) 880-883
61. Biem, A., Katagiri, S., Juang, B.H.: Pattern recognition using discriminative feature extraction. IEEE Trans. Signal Processing 45(2) (1997) 500-504
62. Huo, Q., Ge, Y., Feng, Z.D.: High performance Chinese OCR based on Gabor features, discriminative feature extraction and model training. In: Proc. ICASSP'01, Salt Lake City, Utah, 3 (2001) 1517-1520
63. Liu, C.L., Mine, R., Koga, M.: Building compact classifier for large character set recognition using discriminative feature extraction. In: Proc. 8th ICDAR, Seoul, Korea (2005) 846-850
64. Suen, C.Y., Nadal, C., Legault, R., Mai, T.A., Lam, L.: Computer recognition of unconstrained handwritten numerals. Proc. IEEE 80(7) (1982) 1162-1180
65. Liu, C.L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: Investigation of normalization and feature extraction techniques. Pattern Recognition 37(2) (2004) 265-279
66. Teow, L.N., Loe, K.F.: Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recognition 35(11) (2002) 2355-2364
67. Holmström, L., Koistinen, P., Laaksonen, J., Oja, E.: Neural and statistical classifiers - taxonomy and two case studies. IEEE Trans. Neural Networks 8(1) (1997) 5-17
68. Liu, C.L., Sako, H., Fujisawa, H.: Performance evaluation of pattern classifiers for handwritten character recognition. Int. J. Document Analysis and Recognition 4(3) (2002) 191-204
69. Tsukumo, J., Tanaka, H.: Classification of handprinted Chinese characters using non-linear normalization and correlation methods. In: Proc. 9th ICPR, Rome (1988) 168-171
70. Yamada, H., Yamamoto, K., Saito, T.: A nonlinear normalization method for hanprinted Kanji character recognition - line density equalization. Pattern Recognition 23(9) (1990) 1023-1029
71. Gori, M., Scarselli, F.: Are multilayer perceptrons adequate for pattern recognition and verification? IEEE Trans. Pattern Anal. Mach. Intell. 20(11) (1998) 1121-1132
72. Liu, C.L., Sako, H., Fujisawa, H.: Effects of classifier structures and training regimes on integrated segmentation and recognition of handwritten numeral strings. IEEE Trans. Pattern Anal. Mach. Intell. 26(11) (2004) 1395-1407
73. Liu, C.L., Marukawa, K.: Handwritten numeral string recognition: Character-level training vs. string-level training. In: Proc. 17th ICPR, Cambridge, UK, 1 (2004) 405-408
74. Raina, R., Shen, Y., Ng, A.Y., McCallum, A.: Classification with hybrid generative/discriminative models. In: Advances in Neural Information Processing System 16 (2003)
75. Dahmen, J., Schluter, R., Ney, H.: Discriminative training of Gaussian mixtures for image object recognition. In: Proc. 21st Symposium of German Association for Pattern Recognition, Bonn, Germany (1999) 205-212
76. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learning 54(1) (2004) 45-66
77. Suen, C.Y., Tan, J.: Analysis of error of handwritten digits made by a multitude of classifiers. Pattern Recognition Letters 26(3) (2005) 369-379
78. Polikar, R., Udpa, L., Udpa, A.S., Honavar, V.: Learn++: An incremental learning algorithm for supervised neural networks. IEEE Trans. System Man Cybernet. Part C 31(4) (2001) 497-508
79. Chawla, N.V., Karakoulas, G.: Learning from labeled and unlabeled data: An empirical study across techniques and domains. J. Artificial Intelligence Research 23 (2005) 331-366
80. Duin, R.P.W.: A note on comparing classifiers. Pattern Recognition Letters, 17 (1996) 529-536