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Exploring Text Recognition Segmentation and Detection in Natural Scene Images

Identification, segmentation, and recognition of fonts from real-world images are major challenges in computer vision, particularly due to subtle differences in font shapes, lighting, and backgrounds. This paper aims to provide a comprehensive review of the latest algorithms for text detection, s...

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Main Authors: Wydyanto, ., Maria, Ulfa
Format: Article
Language:English
English
Published: INTI International University 2024
Subjects:
Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://eprints.intimal.edu.my/2104/1/jods2024_66.pdf
http://eprints.intimal.edu.my/2104/2/642
http://eprints.intimal.edu.my/2104/
http://ipublishing.intimal.edu.my/jods.html
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spelling my-inti-eprints.21042024-12-26T06:38:23Z http://eprints.intimal.edu.my/2104/ Exploring Text Recognition Segmentation and Detection in Natural Scene Images Wydyanto, . Maria, Ulfa Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software Identification, segmentation, and recognition of fonts from real-world images are major challenges in computer vision, particularly due to subtle differences in font shapes, lighting, and backgrounds. This paper aims to provide a comprehensive review of the latest algorithms for text detection, segmentation, and recognition from natural scene images. A variety of techniques are assessed for their use in natural settings, including deep learning-based methods, region proposal, and feature-based detection. There is additional discussion of the difficulties of managing changes in text properties such as font type, size, orientation, and noise and occlusion disruptions. This survey also looks at preprocessing techniques like filtering and illumination normalization that are meant to increase the accuracy of text detection. In light of the findings of the literature analysis, this study concludes that the combination of adaptive segmentation techniques with deep learning-based recognition models offers promising performance in text recognition in natural scenery images. This survey provides a foundation for the development of more effective and robust methods for future applications in the fields of image processing and computer vision. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2104/1/jods2024_66.pdf text en cc_by_4 http://eprints.intimal.edu.my/2104/2/642 Wydyanto, . and Maria, Ulfa (2024) Exploring Text Recognition Segmentation and Detection in Natural Scene Images. Journal of Data Science, 2024 (66). pp. 1-18. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
spellingShingle Q Science (General)
QA75 Electronic computers. Computer science
QA76 Computer software
Wydyanto, .
Maria, Ulfa
Exploring Text Recognition Segmentation and Detection in Natural Scene Images
description Identification, segmentation, and recognition of fonts from real-world images are major challenges in computer vision, particularly due to subtle differences in font shapes, lighting, and backgrounds. This paper aims to provide a comprehensive review of the latest algorithms for text detection, segmentation, and recognition from natural scene images. A variety of techniques are assessed for their use in natural settings, including deep learning-based methods, region proposal, and feature-based detection. There is additional discussion of the difficulties of managing changes in text properties such as font type, size, orientation, and noise and occlusion disruptions. This survey also looks at preprocessing techniques like filtering and illumination normalization that are meant to increase the accuracy of text detection. In light of the findings of the literature analysis, this study concludes that the combination of adaptive segmentation techniques with deep learning-based recognition models offers promising performance in text recognition in natural scenery images. This survey provides a foundation for the development of more effective and robust methods for future applications in the fields of image processing and computer vision.
format Article
author Wydyanto, .
Maria, Ulfa
author_facet Wydyanto, .
Maria, Ulfa
author_sort Wydyanto, .
title Exploring Text Recognition Segmentation and Detection in Natural Scene Images
title_short Exploring Text Recognition Segmentation and Detection in Natural Scene Images
title_full Exploring Text Recognition Segmentation and Detection in Natural Scene Images
title_fullStr Exploring Text Recognition Segmentation and Detection in Natural Scene Images
title_full_unstemmed Exploring Text Recognition Segmentation and Detection in Natural Scene Images
title_sort exploring text recognition segmentation and detection in natural scene images
publisher INTI International University
publishDate 2024
url http://eprints.intimal.edu.my/2104/1/jods2024_66.pdf
http://eprints.intimal.edu.my/2104/2/642
http://eprints.intimal.edu.my/2104/
http://ipublishing.intimal.edu.my/jods.html
_version_ 1819915649467547648
score 13.223943

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