  Documents often contain complex physical structures, which make the Document
Layout Analysis (DLA) task challenging. As a pre-processing step for content
extraction, DLA has the potential to capture rich information in historical or
scientific documents on a large scale. Although many deep-learning-based
methods from computer vision have already achieved excellent performance in
detecting \emph{Figure} from documents, they are still unsatisfactory in
recognizing the \emph{List}, \emph{Table}, \emph{Text} and \emph{Title}
category blocks in DLA. This paper proposes a VTLayout model fusing the
documents' deep visual, shallow visual, and text features to localize and
identify different category blocks. The model mainly includes two stages, and
the three feature extractors are built in the second stage. In the first stage,
the Cascade Mask R-CNN model is applied directly to localize all category
blocks of the documents. In the second stage, the deep visual, shallow visual,
and text features are extracted for fusion to identify the category blocks of
documents. As a result, we strengthen the classification power of different
category blocks based on the existing localization technique. The experimental
results show that the identification capability of the VTLayout is superior to
the most advanced method of DLA based on the PubLayNet dataset, and the F1
score is as high as 0.9599.
