Study of Convolutional Neural Network in Recognizing Static American Sign Language Sign language is a form of communication language to connect a deafmute person to the world. It involves the uses of hand gestures and body movement in order to express an idea. Nevertheless, general publics are mostly not educated to comprehend the sign language. For this reason, there is a need to have a translator to facilitate the communication. This paper would like to present a Convolutional Neural Network (CNN) model for predicting American Sign Language. There are 4800 images were captured to train and validate the proposed model. 95% recognition accuracy was attained in experiment, which shows robust performance in recognition 24 static American Sign Language pattern. The successful development of this model can be served as the basis to develop a more complicated sign language translator.

Basic investigation of sign language motion classification by feature extraction using pretrained network models This paper presents a method of classifying sign language motion using the feature elements extracted by using pretrained networks. Good results for the image recognition were obtained using this approach. Sign language motions are diverse and complex, so it is difficult to manually extract appropriate feature elements from them. Furthermore, it is not realistic to collect a lot of sign language motion data for applying to deep learning. Therefore, it is thought that the possibility of sign language recognition system will be greatly enhanced if a pretrained network model can be used. Feature elements of 25 types of sign language motions were extracted using the pretrained network models including AlexNet. Trained models of sign language motions were created by Long Short Time Memory (LSTM) using feature element data, and the classification performance was evaluated. The results confirmed that an average classification rate 70.6% can be obtained with feature elements using the VGG16 network model and the trained model created by LSTM.