  Document layout analysis (DLA) aims to divide a document image into different
types of regions. DLA plays an important role in the document content
understanding and information extraction systems. Exploring a method that can
use less data for effective training contributes to the development of DLA. We
consider a Human-in-the-loop (HITL) collaborative intelligence in the DLA. Our
approach was inspired by the fact that the HITL push the model to learn from
the unknown problems by adding a small amount of data based on knowledge. The
HITL select key samples by using confidence. However, using confidence to find
key samples is not suitable for DLA tasks. We propose the Key Samples Selection
(KSS) method to find key samples in high-level tasks (semantic segmentation)
more accurately through agent collaboration, effectively reducing costs. Once
selected, these key samples are passed to human beings for active labeling,
then the model will be updated with the labeled samples. Hence, we revisited
the learning system from reinforcement learning and designed a sample-based
agent update strategy, which effectively improves the agent's ability to accept
new samples. It achieves significant improvement results in two benchmarks
(DSSE-200 (from 77.1% to 86.3%) and CS-150 (from 88.0% to 95.6%)) by using 10%
of labeled data.
