Classification of Bengali Sign Language Characters by Applying a Novel Deep Convolutional Neural Network

With approximately 466 million deaf-mute people in the world, sign language recognition has been an area of interest for researches for a long time now. Previously, many researchers have conducted studies on different sign language datasets, achieving a decent accuracy of recognition. However, not many studies have been conducted on the Bengali sign alphabet recognition dataset despite having approximately 3 million severe deaf cases alone. Previous studies on Bengali sign character detection suggested decent accuracies using traditional machine learning approaches. However, in recent studies, some researches have achieved higher test accuracy using deep learning models. In our study, we've proposed a novel Convolutional Neural Network (CNN) model for the recognition of the Bengali sign alphabets from the Ishara-Lipi dataset. Our model achieved an overall accuracy of 99.22% that has outperformed all previous studies of Bengali sign character recognition for the deaf-mute community.