SIGN LANGUAGE DETECTION AND TEXT TO SPEECH GENERATION

Abstract

A vital technique for bridging the communication gap between hearing-impaired and normal individuals is sign language. But given the diversity of today's approximately 7000 sign languages, which vary in hand shapes, body part positions, and motion positions, automated sign language recognition (ASLR) is a challenging method. Researchers are looking into more effective ways to build ASLR systems to find intelligent solutions in order to get around this complexity, and they have shown impressive results. This purpose of this work is to examine the literature on intelligent systems for sign language recognition spanning the previous 20 years. 649 publications in all about intelligent systems and decision assistance on the recognition of sign language (SLR) are taken out of the Scopus database and examined. The taken out Using bibliometric VOS Viewer software, articles are evaluated in order to: (1) determine the temporal and spatial distributions of the publications; (2) establish cooperation networks between authors and affiliations; and (3) identify productive institutions within this framework. Additionally, methods for vision-based sign language recognition are reviewed. There is a discussion of the many feature extraction and classification methods utilized in SLR to get high-quality results. This paper's literature assessment highlights the value of integrating intelligent solutions into sign language recognition systems and illustrates that the development of flawless intelligent systems for sign language recognition remains an unsolved challenge. Overall, it is anticipated that this study will aid in the production of intelligent based SLR and the collection of information while offering readers, researchers, and practitioners a road map for future direction.