GRENZE International Journal of Engineering and Technology
Authors:
Alen Charuvila Saji, K.Ramalakshmi, Senbagavalli M, Hemalatha Gunasekaran, Shamila Ebenezer
Volume:
8
Issue:
1
Grenze ID:
01.GIJET.8.1.32
Pages:
441-446
Abstract
The deaf-mute community utilises sign language for interacting among themselves
and others. The introduction of standard sign language has made their lives much easier. This
paper proposes an effective hand-sign recognition method using a deep learning technique and
is based on YOLOv5, which is a real-time object detection algorithm which detects a hand sign
and outputs the corresponding text. The proposed model utilises various sub-models namely,
Cross Stage Partial Network (CSPNet), Path Aggregation Network (PANet), Dense Prediction.
This model can be conveniently deployed into an android application with a user-friendly
interface.