  The document layout analysis (DLA) aims to decompose document images into
high-level semantic areas (i.e., figures, tables, texts, and background).
Creating a DLA framework with strong generalization capabilities is a challenge
due to document objects are diversity in layout, size, aspect ratio, texture,
etc. Many researchers devoted this challenge by synthesizing data to build
large training sets. However, the synthetic training data has different styles
and erratic quality. Besides, there is a large gap between the source data and
the target data. In this paper, we propose an unsupervised cross-domain DLA
framework based on document style guidance. We integrated the document quality
assessment and the document cross-domain analysis into a unified framework. Our
framework is composed of three components, Document Layout Generator (GLD),
Document Elements Decorator(GED), and Document Style Discriminator(DSD). The
GLD is used to document layout generates, the GED is used to document layout
elements fill, and the DSD is used to document quality assessment and
cross-domain guidance. First, we apply GLD to predict the positions of the
generated document. Then, we design a novel algorithm based on aesthetic
guidance to fill the document positions. Finally, we use contrastive learning
to evaluate the quality assessment of the document. Besides, we design a new
strategy to change the document quality assessment component into a document
cross-domain style guide component. Our framework is an unsupervised document
layout analysis framework. We have proved through numerous experiments that our
proposed method has achieved remarkable performance.
