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

• URL: http://arxiv.org/abs/2606.17644v1
• Date: Tue, 16 Jun 2026 08:04:27 GMT
• Title: Bounding Box Label Propagation for Re-Annotation of Document Layout Analysis Datasets
• Abstract summary: Bounding Box Label Propagation (BBLP) is a pseudo-labelling framework for object detection.<n>It produces high-quality class annotations of bounding boxes.<n>It achieves a mAP of 54.0%, corresponding to 81.6% of fully supervised performance, while using only 10% labelled data.
• Score: 49.345169562968415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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