TY  - JOUR
AU  - Thanh Ha Do
AU  - Oriol Ramos Terrades
AU  - Salvatore Tabbone
PY  - 2019//
TI  - DSD: document sparse-based denoising algorithm
T2  - PAA
JO  - Pattern Analysis and Applications
SP  - 177–186
VL  - 22
IS  - 1
KW  - Document denoising
KW  - Sparse representations
KW  - Sparse dictionary learning
KW  - Document degradation models
N2  - 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.
UR  - https://link.springer.com/article/10.1007/s10044-018-0714-3
N1  - DAG; 600.097; 600.140; 600.121
ID  - Thanh Ha Do2019
ER  -