DSD: document sparse-based denoising algorithm

T. Do (1) , O. Ramos Terrades (2) , S. Tabbone (3)

1 USTH - University of Science and Technology of Hanoi
2 UAB - Universitat Autònoma de Barcelona = Autonomous University of Barcelona = Universidad Autónoma de Barcelona
3 LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery

In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising.

Mots clés: Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

T. Do, O. Ramos Terrades, S. Tabbone. DSD: document sparse-based denoising algorithm. Pattern Analysis and Applications, 2019, 22 (1), pp.177-186. ⟨10.1007/s10044-018-0714-3⟩. ⟨hal-02965116⟩