Open AccessComparative Study on Text Segmentation Techniques
Author(s) -
Bharat Patel,
AUTHOR_ID,
Jagin M. Patel,
AUTHOR_ID
Publication year - 2022
Publication title -
ymer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.103
H-Index - 5
ISSN - 0044-0477
DOI - 10.37896/ymer21.01/35
Subject(s) - segmentation , artificial intelligence , scale space segmentation , image segmentation , computer science , segmentation based object categorization , computer vision , connected component labeling , region growing , pattern recognition (psychology) , minimum spanning tree based segmentation , cursive , image texture
Text segmentation, whether printed, handwritten or cursive, is one of the most complicated phases in any OCR. The accuracy of recognition will be heavily reliant on good segmentation. Image segmentation is a crucial component of image analysis and the field of computer vision. Researchers have developed several techniques for segmentation, each of which is used for different types of segmented objects. At present no any universal method is available for image segmentation. Existing image segmentation techniques are not capable to deal with images of any types. This survey looked at a variety of image segmentation techniques, evaluated them, and discussed the issues that came up as a result of using them