Combining Spectral and Spatial Features for Robust Foreground-Background Separation
Foreground-background separation in multispectral images of damaged manuscripts can benefit from both, spectral and spatial information. Therefore, we incorporate a Markov Random Field which provides a powerful tool to combine both features simultaneously. Higher order models enable the inclusion of spatial constraints based on stroke characteristics. We apply belief propagation for inference and include the higher order potentials by upgrading the message update. The proposed segmentation method requires no training and is independent of script, size, and style of characters. We will demonstrate the robust performance on a set of degraded documents and on synthetic images.