Prediction of Turkish Sign Language Alphabets Utilizing Deep Learning Method

Sign Language (SL) is an important tool for communicating the hearing-impaired persons. Therefore, many sign languages exist depending on the language of the country. This study proposes using Turkish sign language (TSL) alphabets to exploit the evolution of deep learning (DL) science to convert mimes of sign language to text. Therefore, a new dataset is created with novelty in the type and conditions. The created dataset includes different conditions in backgrounds, lighting, and positions. Then, this paper suggests Convolutional Neural Networks (CNNs) models detect and classify the TSL alphabets signs. Moreover, the research tries to apply different hyperparameters to the developed models to get the best result. The best model of CNN contains five convolutional layers and uses the Adam learning rate method to adjust the accuracy. This model gives 97.80% accuracy after 70 epochs. Consequently, the suggested models with the challenging dataset are an important point for developing the sign language recognition field.