  Document images often have intricate layout structures, with numerous content
regions (e.g. texts, figures, tables) densely arranged on each page. This makes
the manual annotation of layout datasets expensive and inefficient. These
characteristics also challenge existing active learning methods, as image-level
scoring and selection suffer from the overexposure of common objects.Inspired
by recent progresses in semi-supervised learning and self-training, we propose
an Object-Level Active Learning framework for efficient document layout
Annotation, OLALA. In this framework, only regions with the most ambiguous
object predictions within an image are selected for annotators to label,
optimizing the use of the annotation budget. For unselected predictions, the
semi-automatic correction algorithm is proposed to identify certain errors
based on prior knowledge of layout structures and rectifies them with minor
supervision. Additionally, we carefully design a perturbation-based object
scoring function for document images. It governs the object selection process
via evaluating prediction ambiguities, and considers both the positions and
categories of predicted layout objects. Extensive experiments show that OLALA
can significantly boost model performance and improve annotation efficiency,
given the same labeling budget. Code for this paper can be accessed via
https://github.com/lolipopshock/detectron2_al.
