Considering the fact that each country has a different and unique sign language, the KETI sign language dataset can be the starting point for further research on the Korean sign language translation. In this paper, we introduce the KETI (Korea Electronics Technology Institute) sign language dataset, which consists of 14,672 videos of high resolution and quality. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. It is well-known that many problems in the field of computer vision require a massive dataset to train deep neural network models. We propose a sign language translation system based on human keypoint estimation.

This shows potential for further improvement in SLT by either jointly training the SLR and translation systems or by revising the gloss annotation system. Our findings also demonstrate that end-to-end translation on predicted glosses provides even better performance than translation on ground truth glosses. On the ASLG-PC12 corpus, we report an improvement of over 16 points in BLEU-4. Our methodology improves on the current state-of-the-art by over 5 and 7 points respectively in BLEU-4 score on ground truth glosses and by using an STMC network to predict glosses of the RWTH-PHOENIX-Weather 2014T dataset. We perform experiments on RWTH-PHOENIX-Weather 2014T, a challenging SLT benchmark dataset of German sign language, and ASLG-PC12, a dataset involving American Sign Language (ASL) recently used in gloss-to-text translation. We report a wide range of experimental results for various Transformer setups and introduce the use of Spatial-Temporal Multi-Cue (STMC) networks in an end-to-end SLT system with Transformer. This paper focuses on the translation system and improves performance by utilizing Transformer networks. Though SLT has gathered interest recently, little study has been performed on the translation system. Then, a translation system generates spoken language translations from the sign language glosses. Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR) system to extract sign language glosses from videos.

Translate english to cybertronian