MediaPipe's Landmarks with RNN for Dynamic Sign Language Recognition

Abstract

Communication for hearing-impaired communities is an exceedingly challenging task, which is why dynamic sign language was developed. Hand gestures and body movements are used to represent vocabulary in dynamic sign language. However, dynamic sign language faces some challenges, such as recognizing complicated hand gestures and low recognition accuracy, in addition to each vocabulary's reliance on a series of frames. This paper used MediaPipe in conjunction with RNN models to address dynamic sign language recognition issues. MediaPipe was used to determine the location, shape, and orientation by extracting keypoints of the hands, body, and face. RNN models such as GRU, LSTM, and Bi-directional LSTM address the issue of frame dependency in sign movement. Due to the lack of video-based datasets for sign language, the DSL10-Dataset was created. DSL10-Dataset contains ten vocabularies that were repeated 75 times by five signers providing the guiding steps for creating such one. Two experiments are carried out on our dataset (DSL10-Dataset) using RNN models to compare the accuracy of dynamic sign language recognition with and without the use of face keypoints. Experiments revealed that our model had an accuracy of more than 99%.