  In this paper, we present StrucTexTv2, an effective document image
pre-training framework, by performing masked visual-textual prediction. It
consists of two self-supervised pre-training tasks: masked image modeling and
masked language modeling, based on text region-level image masking. The
proposed method randomly masks some image regions according to the bounding box
coordinates of text words. The objectives of our pre-training tasks are
reconstructing the pixels of masked image regions and the corresponding masked
tokens simultaneously. Hence the pre-trained encoder can capture more textual
semantics in comparison to the masked image modeling that usually predicts the
masked image patches. Compared to the masked multi-modal modeling methods for
document image understanding that rely on both the image and text modalities,
StrucTexTv2 models image-only input and potentially deals with more application
scenarios free from OCR pre-processing. Extensive experiments on mainstream
benchmarks of document image understanding demonstrate the effectiveness of
StrucTexTv2. It achieves competitive or even new state-of-the-art performance
in various downstream tasks such as image classification, layout analysis,
table structure recognition, document OCR, and information extraction under the
end-to-end scenario.
