cvpr cvpr2013 cvpr2013-22 cvpr2013-22-reference knowledge-graph by maker-knowledge-mining
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Author: Róisín Rowley-Brooke, François Pitié, Anil Kokaram
Abstract: This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subsequent image labelling. Finally restoration of the degraded regions is performed using exemplar-based image inpainting. The proposed method is evaluated visually and numerically on a freely available database of 25 scanned manuscript image pairs with ground truth, and is shown to outperform recent non-blind bleed-through removal techniques.
[1] J. A. Burgoyne, J. Devaney, L. Pugin, and I. Fujinaga. Enhanced bleedthrough correction for early music documents with recto-verso registration. In Int. Conf. Music Inform. Retrieval, pages 407–412, Philadelphia, PA, 2008.
[2] P. Castro, R. J. Almeida, and J. R. C. Pinto. Restoration of double-sided ancient music documents with bleed-through. In L. Rueda, D. Mery, and J. Kittler, editors, CIARP, volume 4756 of LNCS, pages 940–949. Springer Berlin/Heidelberg, 2007.
[3] A. Criminisi, P. Perez, and K. Toyama. Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process., 13(9): 1200–1212, 2004.
[4] F. Drira, F. Le Bourgeois, and H. Emptoz. Restoring ink bleed-through degraded document images using a recursive unsupervised classification technique. In H. Bunke and A. Spitz, editors, DAS, volume 3872 of LNCS, pages 38–49. Springer Berlin / Heidelberg, 2006.
[5] R. Estrada and C. Tomasi. Manuscript bleed-through removal via hysteresis thresholding. In 10th Int. Conf. Doc. Anal. and Recogn., pages 753–757, Barcelona, Spain, 2009.
[6] B. Gatos, I. Pratikakis, and S. J. Perantonis. Adaptive degraded document image binarization. J. Pattern Recogn. , 39(3):317–327, 2006.
[7] Y. Huang, M. S. Brown, and D. Xu. A framework for reduc-
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17] ing ink-bleed in old documents. In IEEE Conf. Comput. Vis. Pattern Recogn. , pages 1–7, Anchorage, AK, 2008. Y. Huang, M. S. Brown, and D. Xu. User-assisted ink-bleed reduction. IEEE Trans. Image Process., 19(10):2646–2658, 2010. R. F. Moghaddam and M. Cheriet. Low quality document image modeling and enhancement. Int. J. Doc. Anal. Recogn., 11(4): 183–201, 2009. R. F. Moghaddam and M. Cheriet. A variational approach to degraded document enhancement. IEEE Trans. Pattern Anal. Mach. Intell., 32(8): 1347–1361, 2010. C. Rother, V. Kolmogorov, V. Lempitsky, and M. Szummer. Optimizing binary mrfs via extended roof duality. In Computer Vision andPattern Recognition, 2007. CVPR ’07. IEEE Conference on, pages 1–8, 2007. R. Rowley-Brooke and A. Kokaram. Bleed-through removal in degraded documents. In SPIE: Doc. Recogn. Retrieval Conf., San Francisco, CA, 2012. R. Rowley-Brooke, F. Piti e´, and A. Kokaram. A ground truth bleed-through document image database. In P. Zaphiris, G. Buchanan, E. Rasmussen, and F. Loizides, editors, TPDL, volume 7489 of LNCS, Paphos, Cyprus, 2012. Springer. J. Sauvola and M. Pietik¨ ainen. Adaptive document image binarization. J. Pattern Recogn., 33(2):225–236, 2000. T. N. Tideman. Independence of clones as a criterion for voting rules. J. Soc. Choice Welf., 4(3):185–206, 1987. A. Tonazzini. Color space transformations for analysis and enhancement of ancient degraded manuscripts. J. Pattern Recogn. Image Anal., 20(3):404–417, 2010. A. Tonazzini, L. Bedini, and E. Salerno. Independent component analysis for document restoration. Int. J. Doc. Anal. Recogn., 7(1): 17–27, 2004.
[18] A. Tonazzini, E. Salerno, and L. Bedini. Fast correction of bleed-through distortion in grayscale documents by a blind source separation technique. Int. J. Doc. Anal. Recogn. , 10(1): 17–25, 2007.
[19] C. Wolf. Document ink bleed-through removal with two hidden markov random fields and a single observation field. IEEE Trans. Pattern Anal. Mach. Intell., 32(3):431–447, 2010. 222999556088