Human-In-The-Loop Document Layout Analysis

Abstract

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.

Designing a Human-in-the-Loop System for Object Detection in Floor Plans

“…Unlike the approach of Keswani et al (2022), our technique does not require to train an additional deferral model for the collaboration of multiple agents in the system. Second, Wu, Ma, Li, Chen & He (2021) have recently proposed the collaboration between two ML models in an HITL system. Their approach defers instances to human experts based on the alignment of these ML models as part of a multi-model collaboration.…”