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
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;
Conference_Titel :
Audio, Language and Image Processing (ICALIP), 2014 International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-3902-2
DOI :
10.1109/ICALIP.2014.7009799