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

Robot calligraphy is an emerging exploration of artificial intelligence inthe fields of art and education. Traditional calligraphy generation researchesmainly focus on methods such as tool-based image processing, generative models,and style transfer. Unlike the English alphabet, the number of Chinesecharacters is tens of thousands, which leads to difficulties in the generationof a style consistent Chinese calligraphic font with over 6000 characters. Dueto the lack of high-quality data sets, formal definitions of calligraphyknowledge, and scientific art evaluation methods, The results generated arefrequently of low quality and falls short of professional-level requirements.To address the above problem, this paper proposes an automatic calligraphygeneration model based on deep generative adversarial networks (deepGAN) thatcan generate style calligraphy fonts with professional standards. The keyhighlights of the proposed method include: (1) The datasets use ahigh-precision calligraphy synthesis method to ensure its high quality andsufficient quantity; (2) Professional calligraphers are invited to conduct aseries of Turing tests to evaluate the gap between model generation results andhuman artistic level; (3) Experimental results indicate that the proposed modelis the state-of-the-art among current calligraphy generation methods. TheTuring tests and similarity evaluations validate the effectiveness of theproposed method.

Style Generation in Robot Calligraphy with Deep Generative Adversarial Networks