  We propose DocFormerv2, a multi-modal transformer for Visual Document
Understanding (VDU). The VDU domain entails understanding documents (beyond
mere OCR predictions) e.g., extracting information from a form, VQA for
documents and other tasks. VDU is challenging as it needs a model to make sense
of multiple modalities (visual, language and spatial) to make a prediction. Our
approach, termed DocFormerv2 is an encoder-decoder transformer which takes as
input - vision, language and spatial features. DocFormerv2 is pre-trained with
unsupervised tasks employed asymmetrically i.e., two novel document tasks on
encoder and one on the auto-regressive decoder. The unsupervised tasks have
been carefully designed to ensure that the pre-training encourages
local-feature alignment between multiple modalities. DocFormerv2 when evaluated
on nine datasets shows state-of-the-art performance over strong baselines e.g.
TabFact (4.3%), InfoVQA (1.4%), FUNSD (1%). Furthermore, to show generalization
capabilities, on three VQA tasks involving scene-text, Doc- Formerv2
outperforms previous comparably-sized models and even does better than much
larger models (such as GIT2, PaLi and Flamingo) on some tasks. Extensive
ablations show that due to its pre-training, DocFormerv2 understands multiple
modalities better than prior-art in VDU.
