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Improvement of Image Binarization Methods Using Image Preprocessing with Local Entropy Filtering for Alphanumerical

Hubert Michalak1, Krzysztof Okarma1

  • 1Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, 70-313 Szczecin, Poland.

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|December 3, 2020
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Summary

Shadows in natural images challenge automatic text recognition. This study introduces local image entropy filtering to improve binarization and enhance optical character recognition (OCR) performance on unevenly lit documents.

Keywords:
image binarizationimage entropyimage preprocessinglocal entropy filteroptical character recognitionthresholding

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Uncontrolled lighting and shadows significantly degrade text recognition in natural images.
  • Standard optical character recognition (OCR) methods struggle with binarization under uneven illumination.
  • Existing adaptive binarization techniques often fail to produce satisfactory results for challenging document images.

Purpose of the Study:

  • To propose an image preprocessing methodology for improving text recognition in uncontrolled lighting.
  • To enhance the performance of classical and adaptive thresholding methods for OCR.
  • To address the limitations of current binarization techniques in shadowed or unevenly lit document images.

Main Methods:

  • Implementation of a local image entropy filtering technique for preprocessing.
  • Application of the proposed filtering to improve various image thresholding methods.
  • Verification of the approach using a dataset of 140 diverse document images.
  • Evaluation of text recognition accuracy using Levenshtein distance and F-Measure.

Main Results:

  • The local image entropy filtering significantly improves the effectiveness of common thresholding methods.
  • Experimental results demonstrate enhanced text recognition accuracy on challenging, unevenly illuminated images.
  • The proposed preprocessing approach shows promising performance gains for OCR tasks.

Conclusions:

  • Local image entropy filtering is an effective preprocessing step for improving OCR in adverse lighting conditions.
  • The methodology offers a valuable solution for enhancing text recognition from natural images with shadows and uneven illumination.
  • The approach provides a robust enhancement for various thresholding techniques, leading to better character visibility and classification.