A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

Abstract

Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived models are integral to modern artificial intelligence recognition methods. In addition, technologies that were widely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods. Sign language data collection includes acquiring data from data gloves, data sensors (such as Kinect, Leap Motion, etc.), and high-definition photography. Meanwhile, facial expression recognition, complex background processing, and 3D sign language recognition have also attracted research interests among scholars. Due to the uniqueness and complexity of Chinese sign language, accuracy, robustness, real-time performance, and user independence are significant challenges for future sign language recognition research. Additionally, suitable datasets and evaluation criteria are also worth pursuing.