English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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