American Sign Language Recognition Using a Multimodal Transformer Network

The North American Deaf community, who commonly communicates using American Sign Language (ASL), faces obstacles regarding education, employment, and access to mental health services [1], [2], [3]. The invention of sign language translation services could assist in reducing the cultural disconnect felt by the Deaf community and encourage hearing individuals to learn sign language. Sign language translation is performed using a camera or video feed to capture a person’s hand signs while performing ASL. The visual data are analyzed using computer vision technologies and passed through neural networks for recognition prediction. This paper proposes a scalable proof-of-concept deep learning solution that can recognize hand signs when trained on limited datasets using a unique Multimodal Transformer Network (MTN) approach. This architecture improves the learning and recognition process by using combined and partitioned skeletal landmarks simultaneously, while further expanding context with Convolutional Neural Network (CNN) features to include relevant information not found within the skeletal data. MediaPipe Holistic [4] and the ResNet50 ImageNet [5] model extract skeletal landmarks and CNN feature data from video feeds, respectively. These data are used as input channels spread among five modalities, achieving an accuracy of 94% with 22 classifications when trained on a dataset with little variety.