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DocCPLNet: Document Image Rectification via Control Point and Illumination Correction.

Hongyin Ni1, Jiayu Han1, Chiyuan Wang1

  • 1School of Computer Science, Northeast Electric Power University, Jilin 132012, China.

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|October 29, 2025
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Summary
This summary is machine-generated.

This study introduces a new method to fix distorted and shadowed document images captured on mobile devices. The technique improves image quality, enhancing optical character recognition (OCR) accuracy and readability.

Keywords:
control pointsgeometric distortionsgeometric unwarpingillumination correctionspatial attention

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Area of Science:

  • Computer Vision
  • Image Processing
  • Document Analysis

Background:

  • Mobile device adoption necessitates efficient document digitization.
  • Image distortions and shadows degrade document quality, impacting OCR and readability.
  • Existing methods struggle with complex distortion and shadow removal.

Purpose of the Study:

  • To propose a novel method for rectifying geometric distortions and eliminating shadows in mobile-captured document images.
  • To enhance image quality for improved optical character recognition (OCR) accuracy and human readability.
  • To develop a robust solution applicable across diverse document capture scenarios.

Main Methods:

  • Utilizing control points and illumination prediction for distortion rectification and shadow elimination.
  • Employing spatial attention for interpolation between control and reference points to correct geometric distortions.
  • Applying an illumination correction model post-geometric unwarping to remove shadows and enhance clarity.

Main Results:

  • Successfully eliminated geometric distortions in captured document images.
  • Effectively removed shadows and enhanced surface clarity.
  • Demonstrated robust performance across diverse scenarios, achieving competitive results on the DocUNet benchmark dataset.

Conclusions:

  • The proposed method effectively rectifies document distortions and removes shadows from mobile-captured images.
  • Enhanced image quality leads to significant improvements in both human readability and OCR accuracy.
  • The approach offers a competitive solution for real-world document digitization challenges.