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

Document layout analysis has a wide range of requirements across variousdomains, languages, and business scenarios. However, most currentstate-of-the-art algorithms are language-dependent, with architectures thatrely on transformer encoders or language-specific text encoders, such as BERT,for feature extraction. These approaches are limited in their ability to handlevery long documents due to input sequence length constraints and are closelytied to language-specific tokenizers. Additionally, training a cross-languagetext encoder can be challenging due to the lack of labeled multilingualdocument datasets that consider privacy. Furthermore, some layout tasks requirea clean separation between different layout components without overlap, whichcan be difficult for image segmentation-based algorithms to achieve. In thispaper, we present Paragraph2Graph, a language-independent graph neural network(GNN)-based model that achieves competitive results on common document layoutdatasets while being adaptable to business scenarios with strict separation.With only 19.95 million parameters, our model is suitable for industrialapplications, particularly in multi-language scenarios.

PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis