Gesturing is one of the natural modes of human communication. Signs produced by gestures can have a basic meaning coupled with additional information that is layered over the basic meaning of the sign. Sign language is an important example of communicative gestures that are highly structured and well accepted across the world as a communication medium for deaf and dumb. In this paper, an online recognition scheme is proposed to interpret the standard numeric sign language comprising of 10 basic hand symbols. A web camera is used to capture the real time hand movements as input to the system. The basic meaning of the hand gesture is extracted from the input data frame by analysing the shape of the hand, considering its orientation, movement and location to be fixed. The input hand shape is processed to identify the palm structure, fingertips and their relative positions and the presence of the extended thumb. A 2-dimensional skeletal model is generated from the acquired shape information to represent and subsequently interpret the basic meaning of the hand gesture.
Sign language recognition from skeletal data using graph and recurrent neural networks
Department of Mathematics, Universidad Autónoma de Ciudad Juárez (UACJ)（数学系，自治城朱埃雷斯大学）；Department of Electrical ingeniering, Universidad Autónoma de Ciudad Juárez (UACJ)（电气工程系，自治城朱埃雷斯大学）；Instituto Iberoamericano San Patricio（伊比利亚美洲圣帕特里克研究所）
SignDiff: Diffusion Model for American Sign Language Production
Rutgers University（罗格斯大学）；Columbia University（哥伦比亚大学）；National University of Singapore（新加坡国立大学）；Victoria University（维多利亚大学）；Georgia Institute of Technology（佐治亚理工学院）；The University of Texas at Dallas（德州大学达拉斯分校）；University of Central Florida（中佛罗里达大学）
A Diffusion-Based Kinematics and Dynamics Model for 3D Sign Language Production
Department of Computer Science, University of Bari Aldo Moro（巴里阿尔多·莫罗大学计算机科学系）；Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria（拉斯帕尔马斯大加那利大学通信技术发展与创新研究所）
SSLR: A Semi-Supervised Learning Method for Isolated Sign Language Recognition
Information and Computer Science Department, King Fahd University of Petroleum and Minerals（国王法赫德石油和矿产大学信息与计算机科学系）；SDAIA–KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）；Computing Department, Imam Abdulrahman Bin Faisal University（伊玛目阿卜杜勒拉赫曼·本·法伊兹大学计算机系）；ServiceNow
