  Document layout analysis (DLA) plays an important role in information
extraction and document understanding. At present, document layout analysis has
reached a milestone achievement, however, document layout analysis of
non-Manhattan is still a challenge. In this paper, we propose an image layer
modeling method to tackle this challenge. To measure the proposed image layer
modeling method, we propose a manually-labeled non-Manhattan layout
fine-grained segmentation dataset named FPD. As far as we know, FPD is the
first manually-labeled non-Manhattan layout fine-grained segmentation dataset.
To effectively extract fine-grained features of documents, we propose an edge
embedding network named L-E^3Net. Experimental results prove that our proposed
image layer modeling method can better deal with the fine-grained segmented
document of the non-Manhattan layout.
