Detection of Turkish Sign Language Using Deep Learning and Image Processing Methods

Abstract

Sign language is a physical language that enables people with disabilities to communicate using hand and facial gestures. For this reason, it is very important for people with disabilities to express themselves freely in society and to make the sign language understandable to everyone. In this study, the data set was created by taking 10223 images for 29 letters in the Turkish Sign Language Alphabet. Images are made suitable for education by using image enhancement techniques. In the final stage of the study, classification processes on images were carried out by using CapsNet, AlexNet and ResNet-50, DenseNet, VGG16, Xception, InceptionV3, NasNet, EfficentNet, Hitnet, Squeezenet architectures and TSLNet, which was designed for the study. When the deep learning models were examined, it was found that CapsNet and TSLNet models were the most successful models with 99.7% and 99.6% accuracy rates, respectively.