Continuous CRF with Multi-scale Quantization Feature Functions Application to Structure Extraction in Old Newspaper

We introduce quantization feature functions to represent continuous or large range discrete data into the symbolic CRF data representation. We show that doing this convertion in a simple way allows the CRF to automaticaly select discriminative features to achieve best performance. This system is evaluated on a segmentation task of degraded newspapers archives. The results obtained show the ability of the CRF model to deal with numerical features similarly as for symbolic representation thanks to the use of quantization feature functions. The segmentation task is achieved by the definition of a horizontal CRF model dedicated to pixel labelling.