Sign Language Production: A Review

TL;DR

This paper reviews recent advances in Sign Language Production using deep learning, highlighting progress, challenges, and future research directions to facilitate bidirectional communication between deaf and hearing communities.

Contribution

It provides a comprehensive overview of recent deep learning-based methods in Sign Language Production, summarizing achievements, limitations, and future research avenues.

Findings

Deep learning has significantly advanced SLP capabilities.

Current methods face challenges like data scarcity and variability.

Future research should focus on robustness and real-world applications.

Abstract

Sign Language is the dominant yet non-primary 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. This survey aims to briefly summarize recent achievements in SLP, discussing their advantages, limitations, and future directions of research.