Tensor representation learning based image patch analysis for text identification and recognition

Pattern Recognition

Volumn 48, Issue 4, 2015, Pages 1211-1224

  Zhong, Guoqiang ^a   Cheriet, Mohamed ^b  

^a OCEAN UNIVERSITY OF CHINA   (China)

^b Synchromedia Laboratory   (Canada)

Author keywords

Ancient document understanding; Convergence; Tensor representation learning; Text identification; Text recognition

Indexed keywords

DECISION TREES; IMAGE ANALYSIS; TENSORS;

ANCIENT DOCUMENTS; CONVERGENCE; TENSOR REPRESENTATION; TEXT IDENTIFICATION; TEXT RECOGNITION;

CHARACTER RECOGNITION;

EID: 84920654331     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.09.025     Document Type: Article

References (69)

1. F.M. Wahl, K.Y. Wong, R.G. Casey, Block segmentation and text extraction in mixed text/image documents, Comput. Graph. Image Process. 20 (4) (1982) 375-390.
2. D. Wang, S.N. Srihari, Classification of newspaper image blocks using texture analysis, Comput. Vis. Graph. Image Process. 47 (3) (1989) 327-352.
3. A.K. Jain, S.K. Bhattacharjee, Text segmentation using gabor filters for automatic document processing, Mach. Vis. Appl. 5 (3) (1992) 169-184.
4. J. Ohya, A. Shio, S. Akamatsu, Recognizing characters in scene images, IEEE Trans. Pattern Anal. Mach. Intell. 16 (2) (1994) 214-220.
5. Y.Y. Tang, H. Ma, X. Mao, D. Liu, C.Y. Suen, A new approach to document analysis based on modified fractal signature, in: ICDAR, 1995, pp. 567-570.
6. A. Antonacopoulos, R.T. Ritchings, Representation and classification of complex-shaped printed regions using white tiles, in: ICDAR, 1995, pp. 1132-1135.
7. A.K. Jain, Y. Zhong, Page segmentation using texture analysis, Pattern Recognit. 29 (5) (1996) 743-770.
8. A. Yamashita, T. Amano, Y. Hirayama, N. Itoh, S. Katoh, T. Mano, K. Toyokawa, A document recognition system and its applications, IBM J. Res. Dev. 40 (3) (1996) 341-352.
9. J. Liu, Y.Y. Tang, C.Y. Suen, Chinese document layout analysis based on adaptive split-and-merge and qualitative spatial reasoning, Pattern Recognit. 30 (7) (1997) 1265-1278.
10. J.-L. Chen, A simplified approach to the HMM based texture analysis and its application to document segmentation, Pattern Recognit. Lett. 18 (10) (1997) 993-1007.
11. A. Simon, J.-C. Pret, A.P. Johnson, A fast algorithm for bottom-up document layout analysis, IEEE Trans. Pattern Anal. Mach. Intell. 19 (3) (1997) 273-277.
12. W. Chan, G. Coghill, Text analysis using local energy, Pattern Recognit. 34 (12) (2001) 2523-2532.
13. K. Wang, J. Kangas, Character location in scene images from digital camera, Pattern Recognit. 36 (10) (2003) 2287-2299.
14. G. Nagy, S.C. Seth, M. Viswanathan, A prototype document image analysis system for technical journals, IEEE Comput. 25 (7) (1992) 10-22.
15. M.S. Krishnamoorthy, G. Nagy, S.C. Seth, M. Viswanathan, Syntactic segmentation and labeling of digitized pages from technical journals, IEEE Trans. Pattern Anal. Mach. Intell. 15 (7) (1993) 737-747.
16. D.J. Ittner, H.S. Baird, Language-free layout analysis, in: ICDAR, 1993, pp. 336-340.
17. L. O'Gorman, The document spectrum for page layout analysis, IEEE Trans. Pattern Anal. Mach. Intell. 15 (11) (1993) 1162-1173.
18. S. Tsujimoto, H. Asada, Major components of a complete text reading system, Proc. IEEE 80 (7) (1992) 1133-1149.
19. K.Y. Wong, R.G. Casey, F.M. Wahl, Document analysis system, IBM J. Res. Dev. 26 (6) (1982) 647-656.
20. K.-C. Fan, C.-H. Liu, Y.-K. Wang, Segmentation and classification of mixed text/graphics/image documents, Pattern Recognit. Lett. 15 (12) (1994) 1201-1209.
21. S.L. Taylor, D.A. Dahl, M. Lipshutz, C. Weir, L.M. Norton, R.W. Nilson, M.C. Linebarger, Integrating natural language understanding with document structure analysis, Artif. Intell. Rev. 8 (1994) 255-276.
22. K. Etemad, D.S. Doermann, R. Chellappa, Multiscale segmentation of unstructured document pages using soft decision integration, IEEE Trans. Pattern Anal. Mach. Intell. 19 (1) (1997) 92-96.
23. D. Chen, H. Bourlard, J.-P. Thiran, Text identification in complex background using SVM, in: CVPR, 2001, pp. 621-627.
24. V.N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, 1995.
25. S.-W. Lee, D.-S. Ryu, Parameter-free geometric document layout analysis, IEEE Trans. Pattern Anal. Mach. Intell. 23 (11) (2001) 1240-1256.
26. C.-L. Liu, S. Jäger, M. Nakagawa, Online recognition of Chinese characters: the state-of-the-art, IEEE Trans. Pattern Anal. Mach. Intell. 26 (2) (2004) 198-213.
27. H. Fujisawa, Forty years of research in character and document recognition - an industrial perspective, Pattern Recognit. 41 (8) (2008) 2435-2446.
28. M. Cheriet, M.A. El-Yacoubi, H. Fujisawa, D.P. Lopresti, G. Lorette, Handwriting recognition research: twenty years of achievement... and beyond, Pattern Recognit. 42 (12) (2009) 3131-3135.
29. G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313 (5786) (2006) 504-507.
30. I. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 1986.
31. J. Yang, D. Zhang, A.F. Frangi, J.-Y. Yang, Two-dimensional PCA: a new approach to appearance-based face representation and recognition, IEEE Trans. Pattern Anal. Mach. Intell. 26 (1) (2004) 131-137.
32. J. Ye, R. Janardan, Q. Li, Two-dimensional linear discriminant analysis, in: NIPS, 2004, pp. 1569-1576.
33. R.A. Fisher, The use of multiple measurements in taxonomic problems, Ann. Eugen. 7 (7) (1936) 179-188.
34. X. He, D. Cai, P. Niyogi, Tensor subspace analysis, in: NIPS, 2005, pp. 499-506.
35. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, S. Lin, Graph embedding and extensions: a general framework for dimensionality reduction, IEEE Trans. Pattern Anal. Mach. Intell. 29 (1) (2007) 40-51.
36. Y. Fu, T.S. Huang, Image classification using correlation tensor analysis, IEEE Trans. Image Process. 17 (2) (2008) 226-234.
37. G. Zhong, M. Cheriet, Large margin low rank tensor analysis, Neural Comput. 26 (4) (2014) 761-780.
38. C. Jia, G. Zhong, Y. Fu, Low-rank tensor learning with discriminant analysis for action classification and image recovery, in: AAAI, 2014, pp. 1228-1234.
39. C. Jia, Y. Kong, Z. Ding, Y. Fu, Latent tensor transfer learning for RGB-D action recognition, in: ACM Multimedia (MM), 2014.
40. H. Wang, S. Yan, T.S. Huang, X. Tang, A convengent solution to tensor subspace learning, in: IJCAI, 2007, pp. 629-634.
41. T.G. Kolda, B.W. Bader, Tensor decompositions and applications, SIAM Rev. 51 (3) (2009) 455-500.
42. G. Dai, D.-Y. Yeung, Tensor embedding methods, in: AAAI, 2006, pp. 330-335.
43. M. Sugiyama, Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis, J. Mach. Learn. Res. 8 (2007) 1027-1061.
44. J.B. Tenenbaum, V. de Silva, J.C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (5500) (2000) 2319-2323.
45. S.T. Roweis, L.K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science 290 (5500) (2000) 2323-2326.
46. H. Wang, S. Yan, D. Xu, X. Tang, T.S. Huang, Trace ratio vs. ratio trace for dimensionality reduction, in: CVPR, 2007, pp. 1-8.
47. B. Schölkopf, A.J. Smola, K.-R. Müller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput. 10 (5) (1998) 1299-1319.
48. G. Baudat, F. Anouar, Generalized discriminant analysis using a kernel approach, Neural Comput. 12 (10) (2000) 2385-2404.
49. F. Nie, S. Xiang, Y. Jia, C. Zhang, S. Yan, Trace ratio criterion for feature selection, in: AAAI, 2008, pp. 671-676.
50. Y.-F. Li, J.T. Kwok, I.W. Tsang, Z.-H. Zhou, A convex method for locating regions of interest with multi-instance learning, in: ECML/PKDD (2), 2009, pp. 15-30.
51. S. Marčelja, Mathematical description of the responses of simple cortical cells, J. Opt. Soc. Am. 70 (1980) 1297-1300.
52. J.G. Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters, J. Opt. Soc. Am. A: Opt. Image Sci. Vis. 2 (7) (1985) 1160-1169.
53. J. Jones, L. Palmer, An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex, J. Neurophysiol. 58 (1987) 1233-1258.
54. J.G. Daugman, Complete discrete 2-D gabor transforms by neural networks for image analysis and compression, IEEE Trans. Acoust. Speech Signal Process. 36 (7) (1988) 1169-1179.
55. D. Gabor, Theory of communication, J. Inst. Electr. Eng. 93 (1946) 429-457.
56. S.S. Raju, P.B. Pati, A.G. Ramakrishnan, Gabor filter based block energy analysis for text extraction from digital document images, in: DIAL, 2004, pp. 233-243.
57. C. Grigorescu, N. Petkov, M.A. Westenberg, Contour detection based on nonclassical receptive field inhibition, IEEE Trans. Image Process. 12 (7) (2003) 729-739.
58. S.E. Grigorescu, N. Petkov, P. Kruizinga, Comparison of texture features based on gabor filters, IEEE Trans. Image Process. 11 (10) (2002) 1160-1167.
59. S. Yan, D. Xu, Q. Yang, L. Zhang, X. Tang, H.-J. Zhang, Multilinear discriminant analysis for face recognition, IEEE Trans. Image Process. 16 (1) (2007) 212-220.
60. L. Breiman, Random forests, Mach. Learn. 45 (1) (2001) 5-32.
61. L. Breiman, Bagging predictors, Mach. Learn. 24 (2) (1996) 123-140.
62. R.E. Schapire, Y. Freund, P. Bartlett, W.S. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, Ann. Stat. 26 (5) (1998) 1651-1686.
63. A. Liaw, M. Wiener, Classification and regression by randomforest, R. News 2 (3) (2002) 18-22.
64. J. Demšar, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res. 7 (2006) 1-30.
65. Y. Zheng, H. Li, D.S. Doermann, Machine printed text and handwriting identification in noisy document images, IEEE Trans. Pattern Anal. Mach. Intell. 26 (3) (2003) 337-353.
66. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, San Diego, California, 1990.
67. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res. 11 (2010) 3371-3408.
68. R. Raina, A. Battle, H. Lee, B. Packer, A.Y. Ng, Self-taught learning: transfer learning from unlabeled data, in: ICML, 2007, pp. 759-766.
69. S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng. 22 (10) (2010) 1345-1359.