PT Unknown
AU Jordy Van Landeghem
   Ruben Tito
   Lukasz Borchmann
   Michal Pietruszka
   Pawel Joziak
   Rafal Powalski
   Dawid Jurkiewicz
   Mickael Coustaty
   Bertrand Anckaert
   Ernest Valveny
   Matthew Blaschko
   Sien Moens
   Tomasz Stanislawek
TI Document Understanding Dataset and Evaluation (DUDE)
BT 20th IEEE International Conference on Computer Vision
PY 2023
BP 19528
EP 19540
AB 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.
ER