%0 Conference Proceedings
%T Document Understanding Dataset and Evaluation (DUDE)
%A Jordy Van Landeghem
%A Ruben Tito
%A Lukasz Borchmann
%A Michal Pietruszka
%A Pawel Joziak
%A Rafal Powalski
%A Dawid Jurkiewicz
%A Mickael Coustaty
%A Bertrand Anckaert
%A Ernest Valveny
%A Matthew Blaschko
%A Sien Moens
%A Tomasz Stanislawek
%B 20th IEEE International Conference on Computer Vision
%D 2023
%F Jordy Van Landeghem2023
%O DAG
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3948), last updated on Fri, 26 Jan 2024 09:23:47 +0100
%X We call on the Document AI (DocAI) community to re-evaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI.
%U https://openaccess.thecvf.com/content/ICCV2023/html/Van_Landeghem_Document_Understanding_Dataset_and_Evaluation_DUDE_ICCV_2023_paper.html
%U http://refbase.cvc.uab.es/files/LTB2023.pdf
%P 19528-19540