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

Low resource Handwritten Text Recognition (HTR) is a hard problem due to thescarce annotated data and the very limited linguistic information (dictionariesand language models). For example, in the case of historical cipheredmanuscripts, which are usually written with invented alphabets to hide themessage contents. Thus, in this paper we address this problem through a datageneration technique based on Bayesian Program Learning (BPL). Contrary totraditional generation approaches, which require a huge amount of annotatedimages, our method is able to generate human-like handwriting using only onesample of each symbol in the alphabet. After generating symbols, we createsynthetic lines to train state-of-the-art HTR architectures in a segmentationfree fashion. Quantitative and qualitative analyses were carried out andconfirm the effectiveness of the proposed method.

One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition