Abstract
The deaf community commonly uses sign language for communication, a highly flexible way of conveying messages. Sign language involves a limited number of core concepts and assigned gestures. This paper aims to create a sign language system that will enhance communication within the deaf community. The focus is to implement a software model for sign language recognition using a classifier. The approach involves recognizing and analyzing gestures using principal component analysis features and Hu-Moment features. Several classifiers are utilized to measure accuracy and performance.
| Original language | English |
|---|---|
| Title of host publication | 5th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798331529437 |
| DOIs | |
| State | Published - 2024 |
| Event | 5th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2024 - Kuala Lumpur, Malaysia Duration: 30 Oct 2024 → 31 Oct 2024 |
Publication series
| Name | 5th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2024 |
|---|
Conference
| Conference | 5th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2024 |
|---|---|
| Country/Territory | Malaysia |
| City | Kuala Lumpur |
| Period | 30/10/24 → 31/10/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Features
- Hu-moments
- PCA
- Sign Language
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Electrical and Electronic Engineering
- Safety, Risk, Reliability and Quality
- Control and Optimization
- Modeling and Simulation
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