• Monalisha Hati

Because of its capacity to bridge the gap between deaf and hearing individuals, the Sign Language Recognition System (SLR) is widely sought for. Due of many difficulties, a powerful SLR is still unattainable in the actual world. Furthermore, as we all know, the identification of sign languages has become one of the most significant study topics in the realm of human-computer interaction (HCI). As a result, this paper provides an overview of the most important investigations based on the Sign Language recognition system, as well as a discussion of the created system, which is divided into sign capture methods and recognition approaches. By evoking significant issues connected with the established systems, the strengths and drawbacks that support the system working flawlessly or otherwise will be emphasized. Then, based on integrating EMG sensors with a data glove, a new approach for building SLR systems is presented. For assigning word boundaries for streams of words in continuous SLR, this technique uses electromyography signals collected from hand muscles. The suggested method is intended to address the issue of word segmentation, resulting in improved recognition capabilities for the continuous sign recognition system.

Artificial Neural Network, Data-Glove, Hidden Markov Model, Posture, Sign Language Recognition System.

"A Brief Study on Sign Language Recognition System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.522-526, March-2019, Available :http://www.jetir.org/papers/JETIRFT06099.pdf

"A Brief Study on Sign Language Recognition System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp522-526, March-2019, Available at : http://www.jetir.org/papers/JETIRFT06099.pdf