Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition
A 2014 report from Digital Marketing Philippines stated that the number of web applications with visual content as their main product has increased significantly. Image processing technology has also undergone significant growth. One example of this is optical character recognition (OCR), which...
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doaj-f118a265acac4e13b2938cb24369390b2020-11-24T20:42:08ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002017-10-018594795610.14716/ijtech.v8i5.877877Histogram Equalization Implementation in the Preprocessing Phase on Optical Character RecognitionPeter Pangestu0Dennis Gunawan1Seng Hansun2Computer Science Study Program, Faculty of Engineering and Informatics Universitas Multimedia Nusantara, Jl. Scientia Boulevard, Gading Serpong, Tangerang, Banten 15811, IndonesiaComputer Science Study Program, Faculty of Engineering and Informatics Universitas Multimedia Nusantara, Jl. Scientia Boulevard, Gading Serpong, Tangerang, Banten 15811, IndonesiaComputer Science Study Program, Faculty of Engineering and Informatics Universitas Multimedia Nusantara, Jl. Scientia Boulevard, Gading Serpong, Tangerang, Banten 15811, IndonesiaA 2014 report from Digital Marketing Philippines stated that the number of web applications with visual content as their main product has increased significantly. Image processing technology has also undergone significant growth. One example of this is optical character recognition (OCR), which can convert the text on an image to plain text. However, a problem occurs when the image has low contrast and low exposure, which potentially results in information being hidden in the image. To address this problem, histogram equalization is used to enhance the image’s contrast so the hidden information can be shown. Similar to X-ray scanning used in the medical field, histogram equalization processes scanned images that have low brightness and low contrast. In this study, histogram equalization was successfully implemented using OCR preprocessing. The test was done with a dataset that contains dark background images with low light text; the successful outcome resulted in the ability to show 74.95% of the information hidden in the image.http://ijtech.eng.ui.ac.id/article/view/877Contrast enhancementHistogram equalizationImage processingInformation hidingOptical character recognition |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Peter Pangestu Dennis Gunawan Seng Hansun |
spellingShingle |
Peter Pangestu Dennis Gunawan Seng Hansun Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition International Journal of Technology Contrast enhancement Histogram equalization Image processing Information hiding Optical character recognition |
author_facet |
Peter Pangestu Dennis Gunawan Seng Hansun |
author_sort |
Peter Pangestu |
title |
Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition |
title_short |
Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition |
title_full |
Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition |
title_fullStr |
Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition |
title_full_unstemmed |
Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition |
title_sort |
histogram equalization implementation in the preprocessing phase on optical character recognition |
publisher |
Universitas Indonesia |
series |
International Journal of Technology |
issn |
2086-9614 2087-2100 |
publishDate |
2017-10-01 |
description |
A 2014 report from Digital Marketing
Philippines stated that the number of web applications with visual content as
their main product has increased significantly. Image processing technology has
also undergone significant growth. One example of this is optical character
recognition (OCR), which can convert the text on an image to plain text. However,
a problem occurs when the image has low contrast and low exposure, which
potentially results in information being hidden in the image. To address this
problem, histogram equalization is used to enhance the image’s contrast so the
hidden information can be shown. Similar to X-ray scanning used in the medical
field, histogram equalization processes scanned images that have low brightness
and low contrast. In this study, histogram equalization was successfully
implemented using OCR preprocessing. The test was done with a dataset that
contains dark background images with low light text; the successful outcome
resulted in the ability to show 74.95% of the information hidden in the image. |
topic |
Contrast enhancement Histogram equalization Image processing Information hiding Optical character recognition |
url |
http://ijtech.eng.ui.ac.id/article/view/877 |
work_keys_str_mv |
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1716823119761506304 |