A learning framework for the optimization and automation of document binarization methods

Computer Vision and Image Understanding

Volumn 117, Issue 3, 2013, Pages 269-280

  Cheriet, Mohamed ^a   Farrahi Moghaddam, Reza ^a   Hedjam, Rachid ^a  

^a Synchromedia Laboratory   (Canada)

Author keywords

Binarization; Document image processing; Learning machines; Multi level maps; Parametric methods

Indexed keywords

BINARIZATIONS; DATA SETS; DOCUMENT IMAGE PROCESSING; DOCUMENT IMAGES; FEATURE VECTORS; GRID-BASED; LEARNING FRAMEWORKS; LEARNING MACHINES; NUMERICAL FEATURES; OPTIMAL PARAMETER; PARAMETRIC METHOD; SUPPORT VECTOR REGRESSION (SVR);

IMAGE PROCESSING; IMAGE RETRIEVAL;

OPTIMIZATION;

EID: 84872331292     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2012.11.003     Document Type: Article

References (50)

1. A. Antonacopoulos, and A. Downton Special issue on the analysis of historical documents IJDAR 9 2007 75 77 (Pubitemid 46567129)
2. J. Kanai, and H.S. Baird Special issue on document image understanding and retrieval Comput. Vision Image Understand. 70 1998 285 286
3. E. Kavallieratou, and H. Antonopoulou Cleaning and enhancing historical document images Adv. Concepts Intell. Vision Syst. 2005 681 688
4. B. Gatos, I. Pratikakis, and S. Perantonis Adaptive degraded document image binarization Pattern Recogn. 39 2006 317 327 (Pubitemid 41808613)
5. S. Lu, B. Su, and C. Tan Document image binarization using background estimation and stroke edges IJDAR 13 2010 303 314
6. R. Farrahi Moghaddam, and M. Cheriet A multi-scale framework for adaptive binarization of degraded document images Pattern Recogn. 43 2010 2186 2198
7. B. Su, S. Lu, C.L. Tan, Binarization of historical document images using the local maximum and minimum, in: DAS'10, Boston, Massachusetts, pp. 159-166.
8. R. Hedjam, R. Farrahi Moghadam, and M. Cheriet A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images Pattern Recogn. 44 2011 2184 2196
9. R. Farrahi Moghaddam, and M. Cheriet AdOtsu: an adaptive and parameterless generalization of Otsu's method for document image binarization Pattern Recogn. 45 2012 2419 2431
10. B. Gatos, K. Ntirogiannis, and I. Pratikakis DIBCO 2009: document image binarization contest IJDAR 2010 1 10
11. I. Pratikakis, B. Gatos, K. Ntirogiannis, H-DIBCO 2010-handwritten document image binarization competition, in: ICFHR'10, pp. 727-732.
12. R. Farrahi Moghaddam, and M. Cheriet Low quality document image modeling and enhancement IJDAR 11 2009 183 201
13. R. Farrahi Moghaddam, and M. Cheriet A variational approach to degraded document enhancement IEEE Trans. Pattern Anal. Mach. Intell. 32 2010 1347 1361
14. R. Rowley-Brooke, F. Pitié, and A. Kokaram A ground truth bleed-through document image database: theory and practice of digital libraries P. Zaphiris, G. Buchanan, E. Rasmussen, F. Loizides, Lecture Notes in Computer Science vol. 7489 2012 Springer Berlin/Heidelberg 185 196
15. B. Gatos, K. Ntirogiannis, I. Pratikakis, ICDAR 2009 document image binarization contest (DIBCO 2009), in: ICDAR'09, pp. 1375-1382.
16. Y. Chen, and G. Leedham Decompose algorithm for thresholding degraded historical document images IEE Proc.-Vis. Image Signal Process. 152 2005 702 714 (Pubitemid 41812253)
17. P. Williams, C. Soares, J.E. Gilbert, A clustering rule-based approach to predictive modeling, in: ACMSE '10, ACM, Oxford, Mississippi, USA, 2010, pp. 1-5.
18. M.N. Anyanwu, and S.G. Shiva Comparative analysis of serial decision tree classification algorithms Int. J. Comput. Sci. Secur. 3 2009 230 240
19. D. Basak, S. Pal, and D. Patranabis Support vector regression Neural Inform. Process. Lett. Rev. 11 2007 203 224
20. H.-S. Don A noise attribute thresholding method for document image binarization Int. J. Document Anal. Recogn. 4 2001 131 138
21. B. Su, S. Lu, C.L. Tan, A self-training learning document binarization framework, in: ICPR'10, pp. 3187-3190.
22. M. Shokri, H. Tizhoosh, Q(λ)-based image thresholding, in: CVR'04, pp. 504-508.
23. E. Badekas, N. Papamarkos, Estimation of proper parameter values for document binarization, in: Proceeding CGIM 2008, Innsbruck, Austria, pp. 600-061.
24. M. Stommel, G. Frieder, Automatic estimation of the legibility of binarised historic documents for unsupervised parameter tuning, in: ICDAR'11, Beijing, China, pp. 104-108.
25. Y. Zhu, Augment document image binarization by learning, in: ICPR'08, Tampa, FL, USA, pp. 1-4.
26. B. Su, S. Lu, C.L. Tan, Combination of document image binarization techniques, in: ICDAR'11, Beijing, China, pp. 22-26.
27. J.G. Kuk, N.I. Cho, Feature based binarization of document images degraded by uneven light condition, in: ICDAR '09, Barcelona, Spain, pp. 748-752.
28. M. Pilu, S. Pollard, A light-weight text image processing method for handheld embedded cameras, in: BMVC'02, Cardiff, UK, pp. 547-556.
29. C.-H. Chou, W.-H. Lin, and F. Chang A binarization method with learning-built rules for document images produced by cameras Pattern Recogn. 43 2010 1518 1530
30. V. Sokratis, E. Kavallieratou, A tool for tuning binarization techniques, in: ICDAR'11, Beijing, China, pp. 1-5.
31. P. Sarkar, E. Saund, and J. Lin Classifying foreground pixels in document images ICDAR'09 2009 IEEE Computer Society Barcelona, Spain 641 645
32. V. Vapnik Statistical Learning Theory 1998 John Wiley & Sons New York
33. M.M. Adankon, M. Cheriet, Support Vector Machine, Encyclopedia of Biometrics, Springer, 2009, pp. 1303-1308.
34. A.J. Smola, and B. Schölkopf A tutorial on support vector regression Statist. Comput. 14 2004 199 222 (Pubitemid 39063488)
35. J. Sauvola, and M. Pietikäinen Adaptive document image binarization Pattern Recogn. 33 2000 225 236 (Pubitemid 32262190)
36. O. Trier, and A. Jain Goal-directed evaluation of binarization methods IEEE Trans. Pattern Anal. Mach. Intel. 17 1995 1191 1201
37. F. Shafait, D. Keysers, T.M. Breuel, Efficient implementation of local adaptive thresholding techniques using integral images, in: Document Recognition and Retrieval XV, San Jose, CA.
38. R. Farrahi Moghaddam, and M. Cheriet RSLDI: restoration of single-sided low-quality document images Pattern Recogn. 42 2009 3355 3364
39. R. Farrahi Moghaddam, M. Cheriet, Application of multi-level classifiers and clustering for automatic word-spotting in historical document images, in: ICDAR'09, Barcelona, Spain, pp. 511-515.
40. M. Lettner, and R. Sablatnig Higher order MRF for foreground-background separation in multi-spectral images of historical manuscripts DAS'10 2010 ACM Boston, Massachusetts 317 324
41. D.B. Solit BRAF mutation predicts sensitivity to mek inhibition Nature 439 2006 358 362 (Pubitemid 43128863)
42. R. Tibshirani Regression shrinkage and selection via the lasso J. Royal. Statist. Soc. B 58 1996 267 288
43. T.G. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization Mach. Learn. 40 2000 139 157
44. G.E. Hinton, and R.R. Salakhutdinov Reducing the dimensionality of data with neural networks Science 313 2006 504 507 (Pubitemid 44148451)
45. N. Otsu A threshold selection method from gray-level histograms IEEE Trans. Syst. Man Cybern. 9 1979 62 66 (Pubitemid 9413341)
46. Y.-T. Pai, Y.-F. Chang, and S.-J. Ruan Adaptive thresholding algorithm: efficient computation technique based on intelligent block detection for degraded document images Pattern Recogn. 43 2010 3177 3187
47. E. Barney Smith An analysis of binarization ground truthing DAS'10 2010 ACM Boston, Massachusetts 27 34
48. E. Barney Smith, C. An, Effect of "ground truth" on image binarization, in: DAS'12, pp. 250-254.
49. M. Cheriet, M. El Yacoubi, H. Fujisawa, D. Lopresti, and G. Lorette Handwriting recognition research: twenty years of achievement... and beyond Pattern Recogn. 42 2009 3131 3135
50. D. Lopresti, G. Nagy, Adapting the turing test for declaring document analysis problems solved, in: DAS'12, pp. 1-5.