DocumentCode
1798602
Title
Binarization of degraded document image using Gaussian Markov random field model
Author
Shujing Lu ; Yue Lu
Author_Institution
Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China
fYear
2014
fDate
7-9 July 2014
Firstpage
272
Lastpage
276
Abstract
This paper presents a binarization approach to degraded document images, which is based on Gaussian Markov Random Field (GMRF) model. The energy function with the single-site and pair-site clique potential functions is formulated for the GMRF. The parameters of the potential functions are estimated by expectation-maximization (EM) algorithm, without necessity of training process. Experiments on different types of degraded document images with various noise, contrast variation or uneven illumination, have demonstrated the validity of the proposed method.
Keywords
Gaussian processes; Markov processes; document image processing; expectation-maximisation algorithm; random processes; GMRF; Gaussian Markov random field model; contrast variation; degraded document image binarization approach; energy function; expectation-maximization algorithm; pair-site clique potential functions; single-site clique potential functions; uneven illumination; Analytical models; Computational modeling; Convergence; Markov random fields; Mathematical model; Pattern recognition; Probability density function; Binarization; Gaussian Markov Random Field; expectation-maximization;
fLanguage
English
Publisher
ieee
Conference_Titel
Audio, Language and Image Processing (ICALIP), 2014 International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4799-3902-2
Type
conf
DOI
10.1109/ICALIP.2014.7009799
Filename
7009799
Link To Document