Bounding Box Label Propagation for Re-Annotation of Document Layout Analysis Datasets

2026-06-16

AI Summary

This work addresses the high cost of re-annotation in document layout analysis caused by evolving label categories by proposing a plug-and-play pseudo-labeling framework tailored for object detection. It introduces label propagation to document layout analysis for the first time, constructing multimodal object representations through the fusion of visual, textual, and positional embeddings. This enables efficient semi-supervised category propagation using only a small set of annotated samples. Experimental results on the D4LA dataset demonstrate that with merely 10% of the labeled data, the method achieves a mean average precision (mAP) of 54.0%, equivalent to 81.6% of the fully supervised performance, thereby substantially reducing manual annotation effort.

Abstract

Datasets in practical document processing scenarios typically grow over time, and their class annotations undergo continuous refinement. This creates significant re-annotation efforts, which are time-consuming and costly. A promising remedy is to re-annotate only a small subset of available documents manually and apply semi-supervised learning techniques that leverage both labelled and unlabelled data. Although there are numerous approaches to tackle this problem for classification, there exists no adaptation for the problem of re-classifying object detection instances, e.g. for document layout analysis. To this end, we propose Bounding Box Label Propagation (BBLP), a pseudo-labelling framework for object detection. An object encoder integrates visual, textual, and positional embeddings from object detection samples to come up with a joint embedding that can be used for Label Propagation on partially annotated datasets in a plug-and-play fashion. Evaluation results indicate that the proposed approach produces high-quality class annotations of bounding boxes. In the D4LA layout analysis dataset, it achieves a mAP of 54.0%, corresponding to 81.6% of fully supervised performance, while using only 10% labelled data. Our work demonstrates the potential of Label Propagation for object detection and lays the groundwork for reducing manual annotation efforts in real-world document processing applications.

Problem

• document layout analysis
• re-annotation
• object detection
• semi-supervised learning
• label propagation

Innovation

• Label Propagation
• Object Detection
• Pseudo-Labeling
• Document Layout Analysis
• Semi-Supervised Learning