Learning Free Document Image Binarization Based on Fast Fuzzy CMeans Clustering In this paper, a novel local threshold binarization method using fast Fuzzy CMeans clustering is proposed. Historical document images with nonuniform background, stains, faded ink are first processed by removing the background using inpainting based method. Then using Fuzzy CMeans clustering is used to cluster out the pixels into three main clusters : sure text pixels, sure background pixels and confused pixels which may or may not be labeled as text. Based on the structural symmetry of pixels (SSP), these confused pixels are then classified into text or background pixels. The SSP is defined as those pixels around strokes whose gradient magnitudes are big enough and whose directions are opposite. As the gradient map is our basis for computing the SSP, we further propose to estimate the background surface first and to extract potential SSP in the compensated image so as to deal with degradations of document images such as uneven illumination, low contrast and stain. To prove the effectiveness of our method, tests on eight public document image datasets are preformed and the experimental results show that our method outperforms other local threshold binarization approaches on both Fmeasure and PSNR.

Blind Source Separation Based Framework for Multispectral Document Images Binarization In this paper, we propose a novel Blind Source Separation (BSS) based framework for multispectral (MS) document images binarization. This framework takes advantage of the multidimensional data representation of MS images and makes use of the Graph regularized Nonnegative Matrix Factorization (GNMF) to decompose MS document images into their different constituting components, i.e., foreground (text, ink), background (paper, parchment), degradation information, etc. The proposed framework is validated on two different realworld data sets of manuscript images showing a high capability of dealing with: variable numbers of bands regardless of the acquisition protocol, different types of degradations, and illumination nonuniformity while outperforming the results reported in the stateoftheart. Although the focus was put on the binary separation (i.e., foregroundbackground), the proposed framework is also used for the decomposition of document images into different components, i.e., background, text, and degradation, which allows full sources separation, whereby further analysis and characterization of each component can be possible. A comparative study is performed using Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methods. Our framework is also validated on another third dataset of MS images of natural objects to demonstrate its generalizability beyond document samples.