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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We present document domain randomization (DDR), the first successful transferof convolutional neural networks (CNNs) trained only on graphically renderedpseudo-paper pages to real-world document segmentation. DDR renderspseudo-document pages by modeling randomized textual and non-textual contentsof interest, with user-defined layout and font styles to support joint learningof fine-grained classes. We demonstrate competitive results using our DDRapproach to extract nine document classes from the benchmark CS-150 and paperspublished in two domains, namely annual meetings of Association forComputational Linguistics (ACL) and IEEE Visualization (VIS). We compare DDR toconditions of style mismatch, fewer or more noisy samples that are more easilyobtained in the real world. We show that high-fidelity semantic information isnot necessary to label semantic classes but style mismatch between train andtest can lower model accuracy. Using smaller training samples had a slightlydetrimental effect. Finally, network models still achieved high test accuracywhen correct labels are diluted towards confusing labels; this behavior holdacross several classes.

Document Domain Randomization for Deep Learning Document Layout Extraction