Deep Learning-Based Technique for Sign Language Detection

Zuriani, Mustaffa and Nik Ahmad Farihin, Mohd Zulkifli and Mohd Herwan, Sulaiman and Ernawan, Ferda and Abbker Adam, Yagoub (2023) Deep Learning-Based Technique for Sign Language Detection. In: 2023 International Conference on Information Technology Research and Innovation (ICITRI) , 16 August 2023 , Jakarta, Indonesia. pp. 167-171.. ISSN 979-8-3503-2494-5 ISBN 979-8-3503-2495-2

	Pdf Deep Learning-Based Technique for Sign Language.pdf Restricted to Repository staff only Download (450kB) | Request a copy
Preview	Pdf Deep Learning-Based Technique.pdf Download (676kB) | Preview

Abstract

Sign languages are a form of communication used by the deaf and hard-of-hearing community. Malay Sign Language (MSL) is the official sign language practiced in Malaysia, enabling communication through hand signs and facial expressions. Each sign and its combination hold a distinct meaning, making it challenging for individuals to casually learn MLS. Therefore, this study presents an object detection model that utilizes the Single Shot Detector (SSD) and Mobilenet to detect MLS in real time. The model focuses solely on detecting static signs that do not involve complex combinations. The datasets used for training consist of 2000 sign images collected from Kaggle website, as well as images captured using a personal camera. The datasets were divided into training, validation, and testing phases in an 80:10:10 ratio, respectively. In conclusion, this study successfully developed a real-time and accurate system for recognizing MSL using the SSD-Mobilenet model. This contribution has significant implications for the field of sign language recognition and can greatly improve communication access for individuals who are deaf or hard-of-hearing.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep Learning, Mobilenet, Sign language detection, Single Shot Detector
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Faculty/Division:	Faculty of Computing Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Miss Amelia Binti Hasan
Date Deposited:	24 Oct 2023 07:07
Last Modified:	24 Oct 2023 07:07
URI:	http://umpir.ump.edu.my/id/eprint/39001
Download Statistic:	View Download Statistics

Actions (login required)

View Item