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: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| 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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| Summary: | 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. |
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