  documents for testing).
document image classification: the RVL-CDIP dataset (a collection of
  400,000 images belonging to one of 16 classes).
document visual question answering: the DocVQA dataset (a collection of 50,000
  questions defined on 12,000+ document images).
The abstract from the paper is the following:
Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to
its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. In this
paper, we present LayoutLMv2 by pre-training text, layout and image in a multi-modal framework, where new model
architectures and pre-training tasks are leveraged. Specifically, LayoutLMv2 not only uses the existing masked
visual-language modeling task but also the new text-image alignment and text-image matching tasks in the pre-training
