A Novel Approach to Classify Bangla Sign Digits using Capsule Network

Communication between hearing impaired and general people is one of the most immense problem nowadays. The main medium of conversation with deaf people is through sign language. But it is an arduous task to learn and communicate with the sign language for any class of people. Over the years, researchers from diverse backgrounds tried to establish a model to automate the process of the detection of sign language. Traditional machine learning technique — k Nearest Neighbor (kNN), Support Vector Machine (SVM) etc. and Neural Network architectures—Back-propagation method, Convolutional Neural Network (CNN), Ensemble Neural Network etc. are existing methods by which the researchers proposed their work. In this paper, we worked on the detection of Bangla sign digits. We adopted and modified Capsule Network by the means of detecting Bangla sign digits. In a capsule network architecture, a capsule is a group of neurons that constitutes the feature vector and spatial properties of the object. A seven-layer of capsule network architecture along with some pre-processing is proposed for the detection. The Ishara-Lipi dataset is used for the training of the model and we get 98.84% accuracy for the classification.