@InProceedings{DavidAldavert2017, author="David Aldavert and Mar{\c{c}}al Rusi{\~n}ol and Ricardo Toledo", title="Automatic Static/Variable Content Separation in Administrative Document Images", booktitle="14th International Conference on Document Analysis and Recognition", year="2017", abstract="In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to matchan incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.", optnote="DAG; 600.084; 600.121", optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3001), last updated on Mon, 07 Dec 2020 14:27:23 +0100", doi="10.1109/ICDAR.2017.23", file=":http://refbase.cvc.uab.es/files/ART2017.pdf:PDF" }