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Images with hidden information data set for information retrieval usage.

Peter Pangestu1, Dennis Gunawan1, Seng Hansun1

  • 1Informatics Department, Universitas Multimedia Nusantara, Tangerang, Indonesia.

Data in Brief
|September 12, 2019
PubMed
Summary
This summary is machine-generated.

Optical Character Recognition (OCR) struggles with low-contrast images. Histogram equalization can improve text recovery in challenging image conditions, enhancing OCR accuracy for degraded documents.

Keywords:
Hidden messagesHistogram equalizationImagesInformation retrievalOCR

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Area of Science:

  • Computer Science
  • Image Processing

Background:

  • Optical Character Recognition (OCR) aims to convert image text to digital data.
  • Low contrast and exposure in images hinder accurate text recovery.
  • Existing OCR methods face challenges with degraded image quality.

Purpose of the Study:

  • To evaluate the effectiveness of histogram equalization as a pre-processing step for OCR.
  • To improve text extraction from low-contrast and low-exposure images.
  • To provide a dataset for further research in OCR pre-processing.

Main Methods:

  • Utilizing histogram equalization to enhance image contrast and exposure.
  • Applying pre-processed images to OCR algorithms.
  • Testing with a dataset of 30 sample images, including previously used and new data.

Main Results:

  • Histogram equalization demonstrated potential in recovering hidden characters.
  • Improved OCR performance on images with low contrast and exposure was observed.
  • The dataset supports the validation of pre-processing techniques for OCR.

Conclusions:

  • Histogram equalization is a viable pre-processing technique for enhancing OCR on degraded images.
  • This method aids in recovering text that would otherwise be lost.
  • The study contributes to improving the robustness of OCR systems.