Recognition of the Hungarian Fingerspelling Alphabet using Convolutional Neural Network based on Depth Data

The aim of this paper is to introduce that Convolutional Neural Networks based on depth data can be applied to recognize the signs of the Hungarian fingerspelling alphabet. After a comparison with end-to-end style RGB images, depth data is chosen as input. With the method recommended in this paper, a 94% classification accuracy was measured for the test subjects.