.png){kind=icon}
.png){kind=icon}
Return
DSD: document sparse-based denoising algorithm
.png){kind=icon}
PRE
AI
DOI：10.1007/s10044-018-0714-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Document denoising
Sparse representations
Sparse dictionary learning
Document degradation models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2
Papers:
1.9K
Citations:
1.9K
View more organizations