Journal of Data Science and Modeling

Static Sign Language Recognition Using Depth Data Based on Geometric Features

Document Type : Research Manuscript

Authors

Zahra Aghajani 1
mostafa karbasi 2
Bahareh asadi 2

1 Department of electrical and computer engineering University of Ghiaseddin Jamshid Kashani, Abyek, Iran

2 Department of electrical and computer engineering University of ghiaseddin Jamshid Kashani, Abyek, Iran

https://doi.org/10.22054/jcsm.2022.63486.1028
Abstract
Deaf people or people with hearing loss have a major problem in everyday communication. There are many applications available in the market to help blind people to interact with the world. Voice-based email and chatting systems are available to communicate with each other by blinds. This helps to interact with persons by blind people. Also, many attempts have been made with Sign Language (SL) translators to solve of communication gap between normal and deaf people and ease communication for deaf people. In this paper, the geometric feature is used as feature extraction for static sign recognition. Support Vector Machine (SVM) classifier is used for training and testing to develop a system using static signs. So, the accuracy result for static signs using the Geometric feature is 62.92\% which needs to be improved by other feature extraction and classifiers.

Keywords

Sign Language SL
SVM
Deaf People

Home

Guide for Authors

Submit Manuscript

Contact Us