Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network

Rehman Ullah, Khan and Hizbullah, Khattak and Sheng Wong, Woei and Hussain, AlSalman and Mogeeb A. A., Mosleh and Sk. Md. Mizanur, Rahman5 (2021) Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network. Computational Intelligence and Neuroscience, 2021 (902301). pp. 1-12. ISSN 1687-5265

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Abstract

(e deaf-mutes population always feels helpless when they are not understood by others and vice versa. (is is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing “Within Blocks” and “Before Classifier” methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models’ efficiency. (e experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. (e CBAM-ResNet “Before Classifier” models are more efficient than “Within Blocks” CBAM-ResNet models. (us, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the “Before Classifier” of CBAMResNet models is more efficient in recognising MSL and it is worth for future research.

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