Document Noise Removal using Sparse Representations over Learned Dictionary

Thanh Ha Do 1 Salvatore Tabbone 1, * Oriol Ramos Terrades 2
* Auteur correspondant
1 QGAR - Querying Graphics through Analysis and Recognition
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
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Communication dans un congrès
The 13th ACM Symposium on Document Engineering, Sep 2013, Florence, Italy. ACM, 2013, 〈10.1145/2494266.2494281〉
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Contributeur : Thanh Ha Do <>
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Dernière modification le : mardi 24 avril 2018 - 13:33:08
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Thanh Ha Do, Salvatore Tabbone, Oriol Ramos Terrades. Document Noise Removal using Sparse Representations over Learned Dictionary. The 13th ACM Symposium on Document Engineering, Sep 2013, Florence, Italy. ACM, 2013, 〈10.1145/2494266.2494281〉. 〈hal-00939174〉

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