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Area of Science:

  • Computer Vision
  • Document Image Analysis

Background:

  • Duplex-printed documents often suffer from show-through artifacts, where reverse-side content is visible on the scanned front side.
  • Existing scanning methods struggle to completely eliminate this unwanted artifact, impacting document clarity and readability.

Purpose of the Study:

  • To develop a novel scanning method that effectively eliminates show-through artifacts in duplex-printed documents.
  • To introduce a fusion-based approach utilizing both white and black backed scans for artifact removal.

Main Methods:

  • A novel method is proposed involving the fusion of a white backed scan image and a black backed scan image.
  • A multilayer perceptron (MLP) is employed to learn the fusion mapping, trained on manually corrected document images.
  • Pixel value histograms of reverse-side and duplex-printed areas are used to quantify show-through severity.

Main Results:

  • The proposed fusion method successfully eliminates the show-through artifact by removing leaked-out reverse-side content.
  • The multilayer perceptron effectively learns the fusion mapping for artifact suppression.
  • Quantitative analysis using histograms demonstrates superior show-through elimination compared to state-of-the-art methods.

Conclusions:

  • The novel fusion technique represents a significant advancement in achieving show-through-free document scanning.
  • The learning-based approach using MLPs provides an effective way to address complex image artifacts.
  • The proposed method offers a robust and quantitatively verifiable solution for improving scanned document quality.