DSD: document sparse-based denoising algorithm

T. H. Do, O. Ramos Terrades, S. Tabbone

Research output: Contribution to journalArticleResearch

Abstract

© 2018, Springer-Verlag London Ltd., part of Springer Nature. 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.
Original languageEnglish
Pages (from-to)177-186
JournalPattern Analysis and Applications
Volume22
DOIs
Publication statusPublished - 5 Feb 2019

Keywords

  • Document degradation models
  • Document denoising
  • Sparse dictionary learning
  • Sparse representations

Fingerprint Dive into the research topics of 'DSD: document sparse-based denoising algorithm'. Together they form a unique fingerprint.

  • Cite this