Rehman Ullah Khan1,*, Woei Sheng Wong1, Insaf Ullah2, Fahad Algarni3, Muhammad Inam Ul Haq4, Mohamad Hardyman bin Barawi1, Muhammad Asghar Khan2
CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2755-2772, 2022, DOI:10.32604/cmc.2022.022471
- 07 December 2021
Abstract The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models More >
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