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Open AccessArticle

FADIT: Fast Document Image Thresholding

by 1,2,3 and 1,3,*
1
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
National Cryosphere Desert Data Center, Lanzhou 730000, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(2), 46; https://doi.org/10.3390/a13020046
Received: 8 February 2020 / Revised: 18 February 2020 / Accepted: 20 February 2020 / Published: 21 February 2020
We propose a fast document image thresholding method (FADIT) and evaluations of the two classic methods for demonstrating the effectiveness of FADIT. We put forward two assumptions: (1) the probability of the occurrence of grayscale text and background is ideally two constants, and (2) a pixel with a low grayscale has a high probability of being classified as text and a pixel with a high grayscale has a high probability of being classified as background. With the two assumptions, a new criterion function is applied to document image thresholding in the Bayesian framework. The effectiveness of the method has been borne of a quantitative metric as well as qualitative comparisons with the state-of-the-art methods. View Full-Text
Keywords: thresholding; document image analysis; image segmentation; Otsu thresholding; document image analysis; image segmentation; Otsu
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MDPI and ACS Style

Min, Y.; Zhang, Y. FADIT: Fast Document Image Thresholding. Algorithms 2020, 13, 46. https://doi.org/10.3390/a13020046

AMA Style

Min Y, Zhang Y. FADIT: Fast Document Image Thresholding. Algorithms. 2020; 13(2):46. https://doi.org/10.3390/a13020046

Chicago/Turabian Style

Min, Yufang; Zhang, Yaonan. 2020. "FADIT: Fast Document Image Thresholding" Algorithms 13, no. 2: 46. https://doi.org/10.3390/a13020046

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