  We propose Universal Document Processing (UDOP), a foundation Document AI
model which unifies text, image, and layout modalities together with varied
task formats, including document understanding and generation. UDOP leverages
the spatial correlation between textual content and document image to model
image, text, and layout modalities with one uniform representation. With a
novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain
downstream tasks into a prompt-based sequence generation scheme. UDOP is
pretrained on both large-scale unlabeled document corpora using innovative
self-supervised objectives and diverse labeled data. UDOP also learns to
generate document images from text and layout modalities via masked image
reconstruction. To the best of our knowledge, this is the first time in the
field of document AI that one model simultaneously achieves high-quality neural
document editing and content customization. Our method sets the
state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA,
across diverse data domains like finance reports, academic papers, and
websites. UDOP ranks first on the leaderboard of the Document Understanding
Benchmark.
