TY - JOUR
AU - 
AB - (IJACSA) International Journal of Advanced Computer Science and Applications, Sign Language Gloss Translation using Deep Learning Models Mohamed Amin, Hesahm Hefny, Ammar Mohammed Department of Computer Science FGSSR, Cairo University, Egypt Abstract—Converting sign language to a form of natural more accurate. The first step toward automating the translation language is one of the recent areas of the machine learning is to formalize the sign language in standard form. There domain. Many research efforts have focused on categorizing are existing several forms of sing languages including Stokoe sign language into gesture or facial recognition. However, these [4], HamNoSys [5], SignWriting [6], and Gloss Notation [7]. efforts ignore the linguistic structure and the context of natural Stokoe notation does not include facial expressions and body sentences. Traditional translation methods have low translation movements. Thus, this sign language is limited and is not quality, poor scalability of their underlying models, and are time- suitable for translation to the deaf. Furthermore, the Ham- consuming. The contribution of this paper is twofold. First, it NoSys form is designed to formalize any sign language using proposes a deep learning approach for bidirectional translation 3D animated avatar. However, it does not provide any easy using GRU 
TI - Sign Language Gloss Translation using Deep Learning Models
JF - International Journal of Advanced Computer Science and Applications
DO - 10.14569/ijacsa.2021.0121178
DA - 2021-01-01
UR - https://www.deepdyve.com/lp/unpaywall/sign-language-gloss-translation-using-deep-learning-models-WOymOjEUyo
DP - DeepDyve
ER -