BANGLA SIGN DIGITS: A DATASET FOR REAL TIME HAND GESTURE RECOGNITION

With current widespread computer vision study, research with a particular culture and aesthetic contexts lead to enhanced performance with improved user experience. Despite a large number of deaf and dumb people, Bangla Sign Language has been less-focused on the research area. This paper proposes the building of identifying Bangla Sign Language using hand gestures to reduce the isolation of the hearing-impairment world. For the last little advancement in deep learning, Convolution Neural Network (CNN) has been used to classify Bangla Sign Language with higher accuracy. Our methodology overcomes all constraints from existing research of Bangla hand gesture recognition. In this paper, we presented a novel real-time method for Bangla sign digits (0-9) that focuses on three sections: image acquisition, preprocessing, and lastly, recognition of Bangla sign digits. For training, we have created a dataset of 1674 images from different people from various conditions. The proposed CNN model architecture classifies all Bangla sign digits with an overall accuracy of 97.63% on real-time video that ensures a potential gesture model that contributed significantly in the domain of Bangla sign number recognition compared to previous researches.