Name
Title | ||
|---|---|---|
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks |
Abstract | ||
|---|---|---|
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 sub-figures. 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDAR.2017.93 | 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | DocType | Volume |
modern deep Convolutional Neural Networks,separation rules,annotated training data,scientific figure mining,data driven approach,compound figure separation,automatic analysis,nontextual paper components,compound multipart figures,separate compound figures,ImageCLEF Medical dataset,Python library | Conference | 01 |
ISSN | ISBN | Citations |
1520-5363 | 978-1-5386-3587-2 | 5 |
PageRank | References | Authors |
0.41 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satoshi Tsutsui | 1 | 20 | 5.84 |
| D. Crandall | 2 | 2111 | 168.58 |