@InProceedings{AdriaMolina2022,
author="Adria Molina
and Lluis Gomez
and Oriol Ramos Terrades
and Josep Llados",
title="A Generic Image Retrieval Method for Date Estimation of Historical Document Collections",
booktitle="Document Analysis Systems.15th IAPR International Workshop, (DAS2022)",
year="2022",
volume="13237",
pages="583--597",
optkeywords="Date estimation",
optkeywords="Document retrieval",
optkeywords="Image retrieval",
optkeywords="Ranking loss",
optkeywords="Smooth-nDCG",
abstract="Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images.",
optnote="DAG; 600.140; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3694), last updated on Thu, 15 Jun 2023 10:26:44 +0200",
doi="10.1007/978-3-031-06555-2_39",
file=":http://refbase.cvc.uab.es/files/MGR2022.pdf:PDF"
}