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

Having a massive community of almost 466 million deaf-mute people all over the world, sign language recognition has always fascinated researchers to develop sophisticated models that can successfully recognize sign languages. Because of not being a universal language, sign language differs in terms of languages and communities. Previously, various researches have been conducted on different sign languages. In this study, we considered the Sign Language MINST dataset. Previously, different classifiers like support vector machine, random forest, multilayer perceptron, etc. have been introduced for sign language recognition. Recently, shallow CNN and Capsule Networks have obtained better results. Therefore, in this research, we proposed a deep convolutional neural network model to achieve the successful identification of the sign linguistics alphabets. After implementing the model, we produced an overall accuracy of 97.62% and comparison with previous researches revealed that our proposed model outperformed all previously introduced models.