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

We propose a sign language translation system based on human keypointestimation. It is well-known that many problems in the field of computer visionrequire a massive amount of dataset to train deep neural network models. Thesituation is even worse when it comes to the sign language translation problemas it is far more difficult to collect high-quality training data. In thispaper, we introduce the KETI (short for Korea Electronics Technology Institute)sign language dataset which consists of 14,672 videos of high resolution andquality. Considering the fact that each country has a different and unique signlanguage, the KETI sign language dataset can be the starting line for furtherresearch on the Korean sign language translation. Using the KETI sign languagedataset, we develop a neural network model for translating sign videos intonatural language sentences by utilizing the human keypoints extracted from aface, hands, and body parts. The obtained human keypoint vector is normalizedby the mean and standard deviation of the keypoints and used as input to ourtranslation model based on the sequence-to-sequence architecture. As a result,we show that our approach is robust even when the size of the training data isnot sufficient. Our translation model achieves 93.28% (55.28%, respectively)translation accuracy on the validation set (test set, respectively) for 105sentences that can be used in emergency situations. We compare several types ofour neural sign translation models based on different attention mechanisms interms of classical metrics for measuring the translation performance.

Neural Sign Language Translation based on Human Keypoint Estimation