A pixel labeling approach for historical digitized books

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Volumn , Issue , 2013, Pages 817-821

  Mehri, Maroua ^a   Heroux, Pierre ^b   Gomez Kramer, Petra ^a   Boucher, Alain ^a   Mullot, Remy ^a  

^a UNIVERSITÉ DE LA ROCHELLE   (France)

^b UNIVERSITÉ DE ROUEN   (France)

Author keywords

autocorrelation; clustering accuracy metrics; consensus clustering; Historical books; homogeneity; multiresolution; pixel labeling; texture

Indexed keywords

CLUSTERING ACCURACY; CONSENSUS CLUSTERING; HISTORICAL BOOKS; HOMOGENEITY; MULTIRESOLUTION; PIXEL LABELING;

AUTOCORRELATION; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; COMPUTER AIDED ANALYSIS; PIXELS; TOOLS;

TEXTURES;

EID: 84889588853     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2013.167     Document Type: Conference Paper

References (30)

1. K. Wong, R. Casey, and F. Wahl, "Document Analysis System," IBM Journal of Research and Development, pp. 647-656, 1982.
2. T. Pavlidis and J. Zhou, "Page Segmentation and Classification, " Graphical Model and Image Processing, pp. 484-496, 1992.
3. S. Khedekar, V. Ramanaprasad, S. Setlur, and V. Govindaraju, "Text-Image Separation in Devanagari Documents," in IJDAR. IEEE, 2003, pp. 1265-1269.
4. S. Mao, A. Rosenfeld, and T. Kanungo, "Document structure analysis algorithms: a literature survey," in DRR. SPIE, 2003, pp. 197-207.
5. N. Journet, J. Ramel, R. Mullot, and V. Eglin, "Document image characterization using a multiresolution analysis of the texture: application to old documents," IJDAR, pp. 9-18, 2008.
6. C. H. Chen, L. F. Pau, and P. Wang, Texture analysis in The Handbook of Pattern Recognition and Computer Vision, 2nd ed. World Scientific, 1998.
7. B. Julesz, "Visual pattern discrimination," in Information Theory. IEEE, 1962, pp. 84-92.
8. T. Simpson, J. Armstrong, and A. Jarman, "Merged consensus clustering to assess and improve class discovery with microarray data," Boston Medical Center Bioinformatics, pp. 1471-1482, 2010.
9. A. Ouji, Y. Leydier, and F. LeBourgeois, "Chromatic / Achromatic Separation in Noisy Document Images," in ICDAR. IEEE, 2011, pp. 167-171.
10. M. Petrou and P. G. Sevilla, Image Processing : Dealing with texture. John Wiley &Sons, 2006.
11. S. Bres, "Contributions a la quantification des criteres de transparence et d'anisotropie par une approche globale: Application au controle de qualite de materiaux composites," Ph. D. dissertation, Institut National des Sciences Appliques de Lyon, Lyon, France, 1994.
12. M. Mehri, P. Gomez-Kramer, P. Heroux, and R. Mullot, "Old document image segmentation using the autocorrelation function and multiresolution analysis," in DRR. SPIE, 2013.
13. N. Otsu, "A threshold selection method from gray-level histograms," in Systems, Man, and Cybernetics. IEEE, 1979, pp. 62-66.
14. A. Busch, W. W. Boles, and S. Sridharan, "Texture for Script Identification," PAMI, pp. 1720-1732, 2005.
15. L. Shijian and C. L. Tan, "Script and Language Identification in Noisy and Degraded Document Images," PAMI, pp. 14-24, 2008.
16. R. Duda, P. Hart, and D. Stork, Pattern Classification. 2nd Edition Wiley-Interscience, 2001.
17. A. Cornuejols and L. Miclet, Apprentissage artificiel: Concepts et algorithmes. 2nd Edition Eyrolles, 2010.
18. D. J. Ketchen and C. L. Shook, "The application of cluster analysis in Strategic Management Research: An analysis and critique," Strategic Management Journal, pp. 441-458, 1996.
19. S. Monti, P. Tamayo, J. Mesirov, and T. Golub, "Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data," Machine Learning, pp. 91-118, 2003.
20. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley &Sons, 1990.
21. G. N. Lance and W. T. Williams, "A general theory of classificatory sorting strategies 1. Hierarchical systems," The Computer Journal, pp. 373-380, 1967.
22. J. B. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations," in Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, 1967, pp. 281-297.
23. F. Kovacs, C. Legany, and A. Babos, "Cluster validity measurement techniques," in International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases. World Scientific and Engineering Academy and Society, 2006, pp. 388-393.
24. G. Nguyen, M. Coustaty, and J. Ogier, "Stroke feature extraction for lettrine indexing," in IPTA. IEEE, 2010, pp. 355-360.
25. D. E. Knuth, The art of computer programming, volume 3: (2nd ed.) sorting and searching. Addison Wesley Longman Publishing Co, 1997.
26. P. Mahalanobis, "On the generalised distance in statistics," in Proceedings of the National Institute of Sciences of India. NISI, 1936, pp. 49-55.
27. D. L. Davies and D. W. Bouldin, "A Cluster Separation Measure," PAMI, pp. 224-227, 1979.
28. J. Dunn, "Well separated clusters and optimal fuzzy partitions," Journal of Cybernetics, pp. 95-104, 1974.
29. P. C. Saxena and K. Navaneetham, "The effect of cluster size, dimensionality, and number of clusters on recovery of true cluster structure through Chernoff-type faces," Journal of the Royal Statistical Society, The Statistician, pp. 415-425, 1991.
30. E. B. Fowlkes and C. L. Mallows, "A Method for Comparing Two Hierarchical Clusterings," Journal of the American Statistical Association, pp. 553-569, 1983.