Sign language recognition based on dual-path background erasure convolutional neural network

Abstract

Sign language is an important way to provide expression information to people with hearing and speaking disabilities.Therefore, sign language recognition has always been a very important research topic. However, many sign language recognition systems currently require complex deep models and rely on expensive sensors, which limits the application scenarios of sign language recognition. To address this issue, based on computer vision, this study proposed a lightweight, dual-path background erasing deep convolutional neural network (DPCNN) model for sign language recognition. The DPCNN consists of two paths. One path is used to learn the overall features, while the other path learns the background features. Then, the background features are gradually subtracted from the overall features to obtain an effective representation of hand features. The proposed model achieved an accuracy of 99.52% on the ASL Finger Spelling dataset. More importantly, the proposed method can be applied to small terminals, thereby improving the application scenarios of sign language recognition. Through experimental comparison, the dual path background erasure network model proposed in this paper has better generalization ability.