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Multiple Learned Dictionaries based Clustered Sparse Coding for the Super-Resolution of Signle Text Image

Rim Walha 1 Drira Fadoua Frank Le Bourgeois 1 Christophe Garcia 1 Mohamed Adel Alimi
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper addresses the problem of generating a super-resolved version of a low-resolution textual image by using Sparse Coding (SC) which suggests that image patches can be sparsely represented from a suitable dictionary. In order to enhance the learning performance and improve the reconstruction ability, we propose in this paper a multiple learned dictionaries based clustered SC approach for single text image super- resolution. For instance, a large High-Resolution/Low-Resolution (HR/LR) patch pair database is collected from a set of high quality character images and then partitioned into several clusters by performing an intelligent clustering algorithm. Two coupled HR/LR dictionaries are learned from each cluster. Based on SC principle, local patch of a LR image is represented from each LR dictionary generating multiple sparse representations of the same patch. The representation that minimizes the reconstruction error is retained and applied to generate a local HR patch from the corresponding HR dictionary. The performance of the proposed approach is evaluated and compared visually and quantitatively to other existing methods applied to text images. In addition, experimental results on character recognition illustrate that the proposed method outperforms the other methods, involved in this study, by providing better recognition rates.
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Submitted on : Wednesday, June 29, 2016 - 3:48:14 PM
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Rim Walha, Drira Fadoua, Frank Le Bourgeois, Christophe Garcia, Mohamed Adel Alimi. Multiple Learned Dictionaries based Clustered Sparse Coding for the Super-Resolution of Signle Text Image. International Conference on Document Analysis and Recognition (ICDAR 2013), Aug 2013, Washington, United States. pp.234-240, ⟨10.1109/ICDAR.2013.103⟩. ⟨hal-01339188⟩



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