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Noise suppression over bi-level graphical documents by sparse representation

Thanh Ha Do 1, * Salvatore Tabbone 1 Oriol Ramos-Terrades 2 
* Corresponding author
1 QGAR - Querying Graphics through Analysis and Recognition
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
CVC - Computer Vision Center (Centre de visio per computador)
Abstract : In this paper, we explore the use of learning algorithm (K-SVD) for building dictionaries adapted to the image properties. In addition, in our model, we also modeled the energy of the noise basing on the function of the normalized cross-correlation between noised and non noised documents identified in training set. We have evaluated this method on the Grec2005 dataset. The experimental results demonstrate the robustness of our approach by comparing it with state-of-the-art methods.
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Submitted on : Saturday, December 1, 2012 - 2:20:36 AM
Last modification on : Saturday, October 16, 2021 - 11:26:09 AM
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  • HAL Id : hal-00759555, version 1



Thanh Ha Do, Salvatore Tabbone, Oriol Ramos-Terrades. Noise suppression over bi-level graphical documents by sparse representation. Colloque International Francophone sur l'Écrit et le Document - CIFED 2012, Mar 2012, Bordeaux, France. ⟨hal-00759555⟩



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