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 Non-negative 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 real-world 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 non-uniformity while outperforming the results reported in the state-of-the-art. Although the focus was put on the binary separation (i.e., foreground/background), 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.