Turkish Sign Language Recognition Using CNN with New Alphabet Dataset

Sign Language Recognition (SLR), also referred to as hand gesture recognition, is an active area of research in computer vision that aims to facilitate communication between the deaf-mute community and the people who don’t understand sign language. The objective of this study is to take a look at how this problem is tackled specifically for Turkish Sign Language (TSL). For this problem, we present a system based on convolution neural networks (CNN) in real-time however the most important part of this study to be underlined is that we present the first open-source TSL alphabet dataset to our knowledge. This dataset focuses on finger spelling and has been collected from 30 people. We conduct and present experiments with this new and first open-source TSL dataset. Our system scores an average accuracy of 99.5% and the top accuracy value is 99.9% with our dataset. Further tests were conducted to measure the performance of our model in real time and added to the study. Finally, our proposed model is trained on a couple of American Sign Language (ASL) datasets, the results of which turn out to be state-of-the-art. You can access our dataset from https://github.com/tugcetemel1/TSL-Recognition-with-CNN.