All You Need In Sign Language Production

Abstract

Sign Language is the dominant form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. To have more realistic perspectives to sign language, we present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language, the main differences between spoken language and sign language. Furthermore, we present the fundamental components of a bi-directional sign language translation system, discussing the main challenges in this area. Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented. Finally, a general framework for SLP and performance evaluation, and also a discussion on the recent developments, advantages, and limitations in SLP, commenting on possible lines for future research are presented.

Word separation in continuous sign language using isolated signs and post-processing

“…Here, we briefly review recent works in CSLR considering transfer learning. With the advent of deep learning in recent years, many recent approaches achieved state-of-the-art performance using the combination of different deep learning-based models, such as CNN and RNN [4,5,20,25,22,23,16,17,21,1,9,2,14,15,27,6,8,11,26,10,19,18]. More specifically, while many advancements have been obtained in ISLR and CSLR with the capabilities of deep learning-based models, some challenges in both tasks still need to be discussed.…”