Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{158722,
        author = {Drishti V Raje and Isha A Marlecha and Mithali Devadiga and Sanjana M and Kiran Y C and Poornima R M},
        title = {Comparitive Study on Sign Language Recognition System},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {10},
        pages = {447-454},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158722},
        abstract = {WHO shows that about 5% of the world’s total population suffers from hearing and speaking disabilities. These disabled individuals utilize Sign Language as their means of divulgence which is quite difficult for most people to understand and interpret. In order to bridge this communication gap, sign language interpreters may be helpful but they may be unavailable at times or expensive to afford. To make this process easier researchers have worked on sign language recognition systems. The aim of this paper is to draw a comparative study between some of the frequently used algorithms.},
        keywords = {Sign Language, Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short Term Memory(LSTM), Deep Learning, Hand Gestures.},
        month = {},
        }