# A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1703.05105 · HTML: https://mlsanity.com/paper/1703.05105/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2017.

## Built on by (most cited first, 10 of 10)

- [SciCap: Generating Captions for Scientific Figures](https://mlsanity.com/paper/2110.11624/) (2021)
- [Deep Learning for Technical Document Classification](https://mlsanity.com/paper/2106.14269/) (2021)
- [Open-PMC-18M: A High-Fidelity Large Scale Medical Dataset for Multimodal Representation Learning](https://mlsanity.com/paper/2506.02738/) (2025)
- [A Two-stage Framework for Compound Figure Separation](https://mlsanity.com/paper/2101.09903/) (2021)
- [Compound Figure Separation of Biomedical Images with Side Loss](https://mlsanity.com/paper/2107.08650/) (2021)
- [BioFors: A Large Biomedical Image Forensics Dataset](https://mlsanity.com/paper/2108.12961/) (2021)
- [Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023](https://mlsanity.com/paper/2501.19353/) (2025)
- [Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning](https://mlsanity.com/paper/2208.14357/) (2022)
- [ChartParser: Automatic Chart Parsing for Print-Impaired](https://mlsanity.com/paper/2211.08863/) (2022)
- [MatMMExtract: An Open-Source Pipeline for Panel-Level Extraction of Grounded Image-Text Pairs from Materials Science Literature](https://mlsanity.com/paper/2606.29667/) (2026)

## Abstract (arXiv)

A key problem in automatic analysis and understanding of scientific papers is to extract semantic information from non-textual paper components like figures, diagrams, tables, etc. Much of this work requires a very first preprocessing step: decomposing compound multi-part figures into individual subfigures. Previous work in compound figure separation has been based on manually designed features and separation rules, which often fail for less common figure types and layouts. Moreover, few implementations for compound figure decomposition are publicly available. This paper proposes a data driven approach to separate compound figures using modern deep Convolutional Neural Networks (CNNs) to train the separator in an end-to-end manner. CNNs eliminate the need for manually designing features and separation rules, but require a large amount of annotated training data. We overcome this challenge using transfer learning as well as automatically synthesizing training exemplars. We evaluate our technique on the ImageCLEF Medical dataset, achieving 85.9% accuracy and outperforming previous techniques. We have released our implementation as an easy-to-use Python library, aiming to promote further research in scientific figure mining.