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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Reading irregular scene text of arbitrary shape in natural images is still achallenging problem, despite the progress made recently. Many existingapproaches incorporate sophisticated network structures to handle variousshapes, use extra annotations for stronger supervision, or employ hard-to-trainrecurrent neural networks for sequence modeling. In this work, we propose asimple yet strong approach for scene text recognition. With no need to convertinput images to sequence representations, we directly connect two-dimensionalCNN features to an attention-based sequence decoder which guided by holisticrepresentation. The holistic representation can guide the attention-baseddecoder focus on more accurate area. As no recurrent module is adopted, ourmodel can be trained in parallel. It achieves 1.5x to 9.4x acceleration tobackward pass and 1.3x to 7.9x acceleration to forward pass, compared with theRNN counterparts. The proposed model is trained with only word-levelannotations. With this simple design, our method achieves state-of-the-art orcompetitive recognition performance on the evaluated regular and irregularscene text benchmark datasets.

A Holistic Representation Guided Attention Network for Scene Text Recognition