PT Journal
AU Thanh Ha Do
   Oriol Ramos Terrades
   Salvatore Tabbone
TI DSD: document sparse-based denoising algorithm
SO Pattern Analysis and Applications
JI PAA
PY 2019
BP 177–186
VL 22
IS 1
DE Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models
AB 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.
ER