This work proposes a pattern recognition model for static gestures in Malaysian Sign Language
(MSL) based on Machine Learning (ML) techniques. The proposed model is divided into two
phases, namely, data acquisition and data processing. The first phase involves capturing the
required sign data, such as the shape and orientation of the hand, to construct a sensor-based SL
dataset. The dataset is collected using a DataGlove device. This device is used to measure the
motions of the fingers and wrists. Sixty-four features represent each sign in the dataset. The
collected sensory dataset is cleaned in the second phase by removing redundant data. Then, the
features are scaled and normalized to exhibit symmetrical behavior and eliminate outliers. Then,
ten different ML techniques are utilized based on real-time data for SL gesture recognition.
Experimental results confirmed the efficacy of the proposed pattern recognition model compared
with previous work.