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Robust document image binarization technique for degraded document images.

Bolan Su1, Shijian Lu, Chew Lim Tan

  • 1School of Computing, National University of Singapore, Singapore. subolan@comp.nus.edu.sg

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 11, 2012
PubMed
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This study introduces a new document image binarization method using adaptive image contrast to improve text segmentation from degraded documents. The technique achieves high accuracy on challenging datasets, outperforming existing methods.

Area of Science:

  • Computer Vision
  • Image Processing
  • Document Analysis

Background:

  • Segmenting text from degraded document images is difficult due to variations in background and text.
  • Existing methods struggle with diverse document degradations.

Purpose of the Study:

  • To propose a novel document image binarization technique for improved text segmentation.
  • To address challenges posed by degraded document images using adaptive image contrast.

Main Methods:

  • Developed a technique using adaptive image contrast, combining local image contrast and gradient.
  • Constructed an adaptive contrast map, binarized it, and combined with Canny's edge map.
  • Segmented text using a local threshold estimated from detected text stroke edge pixels.

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Main Results:

  • Achieved high accuracies on DIBCO 2009 (93.5%), DIBCO 2011 (87.8%), and handwritten-DIBCO 2010 (92.03%).
  • Demonstrated superior performance on the challenging Bickley diary dataset.
  • Outperformed or matched best-performing methods in recent contests.

Conclusions:

  • The proposed adaptive image contrast method is simple, robust, and requires minimal parameter tuning.
  • The technique effectively handles variations in text and background caused by document degradation.
  • This method offers a significant improvement for document image binarization.