Document Understanding Dataset and Evaluation (DUDE)

Jordy Van Landeghem; Rubén Tito; Łukasz Borchmann; Michał Pietruszka; Paweł Józiak; Rafał Powalski; Dawid Jurkiewicz; Mickaël Coustaty; Bertrand Ackaert; Ernest Valveny; Matthew Blaschko; Sien Moens; Tomasz Stanisławek

arXiv · 2305.08455

Document Understanding Dataset and Evaluation (DUDE)

Jordy Van Landeghem·Rubén Tito·Łukasz Borchmann·Michał Pietruszka·Paweł Józiak·Rafał Powalski·Dawid Jurkiewicz·Mickaël Coustaty·Bertrand Ackaert·Ernest Valveny·Matthew Blaschko·Sien Moens·Tomasz Stanisławek

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Abstract

We call on the Document AI (DocAI) community to reevaluate 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.

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Jordy Van Landeghem, Rubén Tito, Łukasz Borchmann, Michał Pietruszka, Paweł Józiak, Rafał Powalski, Dawid Jurkiewicz, Mickaël Coustaty, Bertrand Ackaert, Ernest Valveny, Matthew Blaschko, Sien Moens, Tomasz Stanisławek. 2023-05-15. Document Understanding Dataset and Evaluation (DUDE). https://arxiv.org/abs/2305.08455

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