  The document layout analysis (DLA) aims to split the document image into
different interest regions and understand the role of each region, which has
wide application such as optical character recognition (OCR) systems and
document retrieval. However, it is a challenge to build a DLA system because
the training data is very limited and lacks an efficient model. In this paper,
we propose an end-to-end united network named Dynamic Residual Fusion Network
(DRFN) for the DLA task. Specifically, we design a dynamic residual feature
fusion module which can fully utilize low-dimensional information and maintain
high-dimensional category information. Besides, to deal with the model
overfitting problem that is caused by lacking enough data, we propose the
dynamic select mechanism for efficient fine-tuning in limited train data. We
experiment with two challenging datasets and demonstrate the effectiveness of
the proposed module.
