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/

Zendy Plus

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

Zendy Tools

Zendy Open

The focus of our paper is the identification and correction of non-worderrors in OCR text. Such errors may be the result of incorrect insertion,deletion, or substitution of a character, or the transposition of two adjacentcharacters within a single word. Or, it can be the result of word boundaryproblems that lead to run-on errors and incorrect-split errors. The traditionalN-gram correction methods can handle single-word errors effectively. However,they show limitations when dealing with split and merge errors. In this paper,we develop an unsupervised method that can handle both errors. The method wedevelop leads to a sizable improvement in the correction rates. This tutorialpaper addresses very difficult word correction problems - namely incorrectrun-on and split errors - and illustrates what needs to be considered whenaddressing such problems. We outline a possible approach and assess its successon a limited study.

A Simple and Practical Approach to Improve Misspellings in OCR Text