Decomposing images of document pages into high-level semantic regions (e.g.,
figures, tables, paragraphs), document object detection (DOD) is fundamental
for downstream tasks like intelligent document editing and understanding. DOD
remains a challenging problem as document objects vary significantly in layout,
size, aspect ratio, texture, etc. An additional challenge arises in practice
because large labeled training datasets are only available for domains that
differ from the target domain. We investigate cross-domain DOD, where the goal
is to learn a detector for the target domain using labeled data from the source
domain and only unlabeled data from the target domain. Documents from the two
domains may vary significantly in layout, language, and genre. We establish a
benchmark suite consisting of different types of PDF document datasets that can
be utilized for cross-domain DOD model training and evaluation. For each
dataset, we provide the page images, bounding box annotations, PDF files, and
the rendering layers extracted from the PDF files. Moreover, we propose a novel
cross-domain DOD model which builds upon the standard detection model and
addresses domain shifts by incorporating three novel alignment modules: Feature
Pyramid Alignment (FPA) module, Region Alignment (RA) module and Rendering
Layer alignment (RLA) module. Extensive experiments on the benchmark suite
substantiate the efficacy of the three proposed modules and the proposed method
significantly outperforms the baseline methods. The project page is at
https://github.com/kailigo/cddod.

文档对象检测（DOD）是将文档页面图像分解成高级语义区域（例如，图形、表格、段落）的基础，但在跨域 DOD 中存在挑战，本文旨在建立 a 基准套件以评估跨域 DOD 模型训练和提出方法，通过结合三个新的对齐模块，如特征金字塔对齐模块（FPA）、区域对齐模块（RA）和渲染层对齐模块（RLA），实现从源域中的有标记数据到目标域中的仅未标记数据的检测器转移。