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

Key Information Extraction (KIE) is a challenging multimodal task that aimsto extract structured value semantic entities from visually rich documents.Although significant progress has been made, there are still two majorchallenges that need to be addressed. Firstly, the layout of existing datasetsis relatively fixed and limited in the number of semantic entity categories,creating a significant gap between these datasets and the complex real-worldscenarios. Secondly, existing methods follow a two-stage pipeline strategy,which may lead to the error propagation problem. Additionally, they aredifficult to apply in situations where unseen semantic entity categoriesemerge. To address the first challenge, we propose a new large-scalehuman-annotated dataset named Complex Layout form for key informationEXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entitycategories. To solve the second challenge, we introduce Parallel Pointer-basedNetwork (PPN), an end-to-end model that can be applied in zero-shot andfew-shot scenarios. PPN leverages the implicit clues between semantic entitiesto assist extracting, and its parallel extraction mechanism allows it toextract multiple results simultaneously and efficiently. Experiments on theCLEX dataset demonstrate that PPN outperforms existing state-of-the-art methodswhile also offering a much faster inference speed.

PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts