Sangeet Aggarwal
ABSTRACT
Most of the document pre-processing techniques are parameter dependent. In this paper, we present a novel framework that learns optimal parameters, depending on the nature of the document image content for binarization and text/graphics segmentation. The learning problem has been formulated as an optimization problem using EM algorithm to adaptively learn optimal parameters. Experimental results have established the effectiveness of our approach.
- J. Banerjee, A. M. Namboodiri, and C. V. Jawahar. Contextual restoration of severely degraded document images. In CVPR, pages 517--524. IEEE, 2009.Google Scholar
Cross Ref
- K. C. Fan, C. H. Liu, and Y. K. Wang. Segmentation and classification of mixed text/graphics/image documents. Pattern Recognition Letters, 15(12): 1201--1209, 1994. Google ScholarDigital Library
- R. Cao and C. L. Tan. Text/graphics separation in maps. In Fourth International Workshop on Graphics Recognition Algorithms and Applications, pages 167--177, London, UK, UK, 2002. Springer-Verlag. Google ScholarDigital Library
- S. Chowdhury, S. Mandal, A. Das, and B. Chanda. Segmentation of text and graphics from document images. In Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02, pages 619--623, Washington, DC, USA, 2007. IEEE Computer Society. Google ScholarDigital Library
- L. A. Fletcher and R. Kasturi. A robust algorithm for text string separation from mixed text/graphics images. IEEE Transaction Pattern Analysis Machine Intelligence, 10(6): 910--918, 1988. Google ScholarDigital Library
- B. Gatos, I. Pratikakis, and S. J. Perantonis. Adaptive degraded document image binarization. Pattern Recognition, 39: 317--327, 2006. Google ScholarDigital Library
- A. K. Jain and S. Bhattacharjee. Texture segmentation using gabor filters for automatic document processing. Machine Vision and Application, 5: 169--184, 1992. Google ScholarDigital Library
- N. Journet, V. Eglin, J. Ramel, and R. Mullot. Text/graphic labelling of ancient printed documents. In Proceedings of International Conference on Document Analysis and Recognition, volume 2, pages 1010--1014, August 2005. Google ScholarDigital Library
- S. Kumar, R. Gupta, N. Khanna, S. Chaudhury, and S. D. Joshi. Text extraction and document image segmentation using matched wavelets and mrf model. IEEE Transactions of Image Processing, 16: 2117--2128, August 2007. Google ScholarDigital Library
- W. Niblack. An Introduction to Digital Image Processing. Strandberg Publishing Company, 1985. Google ScholarDigital Library
- N. Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9: 62--66, 1979.Google ScholarCross Ref
- P. P. Roy, J. Llados, and U. Pal. Text/graphics separation in color maps. In Proceedings of the International Conference on Computing: Theory and Applications, pages 545--551, Washington, DC, USA, 2007. IEEE Computer Society. Google ScholarDigital Library
- J. Sauvola and M. Pietikainen. Adaptive document image binarization. Pattern Recognition, 33: 225--236, 2000.Google ScholarCross Ref
- G. Sharma, R. Garg, and S. Chaudhury. Curvature feature distribution based classification of indian scripts from document images. In Proceedings of the International Workshop on Multilingual OCR, pages 3:1--3:6, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- C. L. Tan and P. O. Ng. Text extraction using pyramid. Pattern Recognition, 31: 63--72, 1998.Google ScholarCross Ref
- K. Tombre, S. Tabbone, L. Pélissier, B. Lamiroy, and P. Dosch. Text/graphics separation revisited. In Proceedings of the 5th International Workshop on Document Analysis Systems V, pages 200--211, London, UK, UK, 2002. Springer-Verlag. Google ScholarDigital Library
- F. M. Wahl, K. Y. Wong, and R. G. Casey. Block segmentation and text extraction in mixed text/image documents. In Computer Graphics and Image Processing, volume 20, pages 375--390, 1982.Google Scholar
- H. Yan. Unified formulation of a class of image thresholding techniques. Pattern Recognition, 29: 2025--2032, 1996.Google ScholarCross Ref
Index Terms
- Content directed enhancement of degraded document images
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