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

Irregular scene text recognition has attracted much attention from theresearch community, mainly due to the complexity of shapes of text in naturalscene.  However, recent methods either rely on shape-sensitive modules such asbounding box regression, or discard sequence learning.  To tackle these issues, we propose a pair of coupling modules, termed asCharacter Anchoring Module (CAM) and Anchor Pooling Module (APM), to extracthigh-level semantics from two-dimensional space to form feature sequences.  The proposed CAM localizes the text in a shape-insensitive way by design byanchoring characters individually.  APM then interpolates and gathers features flexibly along the characteranchors which enables sequence learning.  The complementary modules realize a harmonic unification of spatialinformation and sequence learning.  With the proposed modules, our recognition system surpasses previousstate-of-the-art scores on irregular and perspective text datasets, including,ICDAR 2015, CUTE, and Total-Text, while paralleling state-of-the-artperformance on regular text datasets.

A New Perspective for Flexible Feature Gathering in Scene Text Recognition Via Character Anchor Pooling