@InProceedings{V.Poulaind{\textquoteright}Andecy2018,
author="V. Poulain d{\textquoteright}Andecy
and Emmanuel Hartmann
and Mar{\c{c}}al Rusi{\~n}ol",
title="Field Extraction by hybrid incremental and a-priori structural templates",
booktitle="13th IAPR International Workshop on Document Analysis Systems",
year="2018",
pages="251--256",
optkeywords="Layout Analysis",
optkeywords="information extraction",
optkeywords="incremental learning",
abstract="In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori genericmodels. We report in the experimental section our results obtained with a dataset of real invoices.",
optnote="DAG; 600.084; 600.129; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3106), last updated on Thu, 28 Mar 2019 10:26:59 +0100",
doi="10.1109/DAS.2018.29",
file=":http://refbase.cvc.uab.es/files/PHR2018.pdf:PDF"
}