Advancing Bangla Sign Language Recognition Through Deep Learning Techniques

The importance of Bangla Sign Language (BdSL) is growing as the number of users continues to increase. However, research in this field remains underexplored. This study emphasizes the significance of BdSL and proposes an automated interpreter system for detecting and recognizing signs. To address the scarcity of established datasets, we developed a custom video dataset comprising 784 sample videos to facilitate empirical research and model training. These videos capture dynamic signs, enabling more effective detection. We experimented with various deep learning models, incorporating diverse pre-processing and frame extraction techniques. Our proposed 3D Convolutional Neural Network (3D CNN) architecture outperformed other models, achieving an accuracy of 82.6%. The model was further evaluated using multiple performance metrics and tested for some recorded videos. Overall, the classification results were robust across all sign classes, demonstrating the effectiveness of our approach.