A Framework for Malaysian Sign Language Recognition Using Deep Learning Initiatives
Problem: The greatest challenge since the introduction of MSL occur when deaf and hard of hearing people try to communicate using MSL with persons without disabilities who do not use MSL. To break this communication barrier, a substantially number of studies has been done to produce Malaysian Sign Language Recognition system–.
Aims/Objectives: The main of objective of this paper is to develop a Low-Cost Malaysian Sign Language Recognition from Action to Text Framework using Deep Learning.
Methodology/approach: To achieve this objective, we propose a framework consisting of three main modules namely learning module, training module and detection module.
Results/finding: To achieve this aim, the usage of MediaPipe framework was introduced. MediaPipe framework improved image acquisition and simplified image processing stage tremendously. Long short-term memory (LSTM) artificial neural network (ANN) is used as training algorithm in training module and prediction algorithm in detection module.
Implication/impact: Thus, researcher only need to focus on reducing the human intervention and create a scalable machine learning system to realize deep learning.
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