TY  - CONF
AU  - David Aldavert
AU  - Marçal Rusiñol
AU  - Ricardo Toledo
A2  - ICDAR
PY  - 2017//
TI  - Automatic Static/Variable Content Separation in Administrative Document Images
BT  - 14th International Conference on Document Analysis and Recognition
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/ART2017.pdf
UR  - http://dx.doi.org/10.1109/ICDAR.2017.23
N1  - DAG; 600.084; 600.121
ID  - David Aldavert2017
ER  -