FADIT: Fast Document Image Thresholding
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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.
2017 ◽
Vol 2
(1)
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pp. 299-316
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Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features
2017 ◽
Vol 108
(1)
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pp. 307-318
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2020 ◽
Vol 34
(07)
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pp. 11053-11060
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2019 ◽
Vol 33
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pp. 10015-10016
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