MultiSpectral Image Binarization using GMMs

Abstract

MultiSpectral Imaging enhances the study of degraded historical documents. It allows for visualizing washed out or even invisible ink but also improves the automated analysis because of a denser spectral sampling. We present a new methodology for binarization of multispectral document images that groups spectral signatures of different sources by fitting two Gaussian Mixture Models (GMMs) with Expectation Maximization. Both GMMs assign cluster labels to the multispectral samples and the clustering results are combined for the identification of the handwriting regions. The method is evaluated on the ICDAR 2015 MS-TEx dataset. Results on this publicly available benchmarking set are encouraging.