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Analysis of PDE-based binarization model for degraded document images

arXiv 2111.05471 ↗First posted 10 Nov 2021cs.CVcited by 0

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Abstract (arXiv)

This report presents the results of a PDE-based binarization model for degraded document images. The model utilizes an edge and binary source term in its formulation. Results indicate effectiveness for document images with bleed-through and faded text and stains to a lesser extent.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 29 references that are in the map. First posted on arXiv 10 Nov 2021.

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References · 4

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2017A selectional auto-encoder approach for document image binarizationcited by 22
  4. 2018PDNet: Semantic Segmentation integrated with a Primal-Dual Network for Document binarizationcited by 2

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