Sign Language Gesture Classification using Neural Networks

Abstract

Recent studies have demonstrated the power of neural networks for different fields of artificial intelligence. In most fields, such as machine translation or speech recognition, neural networks outperform previously used methods (Hidden Markov Models with Gaussian Mixtures, Statistical Machine Translation, etc.). In this paper, the efficiency of the LeNet convolutional neural network for isolated word sign language recognition is demonstrated. As a preprocessing step, we apply several techniques to obtain the same dimension for the input that contains gesture information. The performance of these preprocessing techniques on a Spanish Sign Language dataset is evaluated. These approaches outperform previously obtained results based on Hidden Markov Models.

Classification of Finger Spelling American Sign Language Using Convolutional Neural Network

“…This means more time-efficiency increases and better results as computers learn more to manage images. Research with the DL method which is currently popular for image processing is the convolutional neural network (CNN) method [8], [9], [14], [15].…”