HandArch: A deep learning architecture for LIBRAS hand configuration recognition

Abstract

Despite the recent advancements in deep learning, sign language recognition persists as a challenge in computer vision due to its complexity in shape and movement patterns. Current studies that address sign language recognition treat hand pose recognition as an image classification problem. Based on this approach, we introduce HandArch, a novel architecture for realtime hand pose recognition from video to accelerate the development of sign language recognition applications. Furthermore, we present Libras91, a novel dataset of Brazilian sign language (LIBRAS) hand configurations containing 91 classes and 108,896 samples. Experimental results show that our approach surpasses the accuracy of previous studies while working in real-time on video files. The recognition accuracy of our system is 99% for the novel dataset and over 95% for other hand pose datasets.