Adaptive thresholding to robust image binarization for degraded document images

2017 ◽  
Author(s):  
Prashant Devidas Ingle ◽  
Parminder Kaur
Keyword(s):  
Adaptive Thresholding ◽  
Document Images ◽  
Image Binarization ◽  
Robust Image ◽  
Degraded Document

scholarly journals Image Binarization for Degraded Document Images

2015 ◽  
Vol 128 (15) ◽  
pp. 38-43
Author(s):  
Sushilkumar N. ◽  
Ulhas B. ◽  
Bhagyashree S.
Keyword(s):  
Document Images ◽  
Image Binarization ◽  
Degraded Document

scholarly journals Document Image Binarization using Image Segmentation Technique

2021 ◽  
Vol 9 (VII) ◽  
pp. 1173-1176
Author(s):  
Mr. Aniket Pagare
Keyword(s):  
Image Segmentation ◽  
Primary Objective ◽  
Document Image ◽  
Document Images ◽  
Image Binarization ◽  
Huge Number ◽  
Document Image Binarization ◽  
Processor Designs ◽  
Equal Work ◽  
Degraded Document

Segmentation of text from badly degraded document images is an extremely difficult assignment because of the high inter/Intra variety between the record foundation and the frontal area text of various report pictures. Picture preparing and design acknowledgment algorithms set aside more effort for execution on a solitary center processor. Designs Preparing Unit (GPU) is more mainstream these days because of its speed, programmability, minimal expense and more inbuilt execution centers in it. The primary objective of this exploration work is to make binarization quicker for acknowledgment of a huge number of corrupted report pictures on GPU. In this framework, we give another picture division calculation that every pixel in the picture has its own limit proposed. We are accomplishing equal work on a window of m*n size and separate article pixel of text stroke of that window. The archive text is additionally sectioned by a nearby edge that is assessed dependent on the forces of identified content stroke edge pixels inside a nearby window.

Binarization of degraded document images based on hierarchical deep supervised network

2018 ◽  
Vol 74 ◽  
pp. 568-586 ◽  
Author(s):  
Quang Nhat Vo ◽  
Soo Hyung Kim ◽  
Hyung Jeong Yang ◽  
Gueesang Lee
Keyword(s):  
Document Images ◽  
Degraded Document
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