Continuous sign language recognition based on hierarchical memory sequence network
Abstract With the goal of solving the problem of feature extractors lacking strong supervision training and insufficient time information concerning single‐sequence model learning, a hierarchical sequence memory network with a multi‐level iterative optimisation strategy is proposed for continuous si...
Main Authors: | Cuihong Xue, Jingli Jia, Ming Yu, Gang Yan, Yingchun Guo, Yuehao Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2024-03-01
|
Series: | IET Computer Vision |
Subjects: | |
Online Access: | https://doi.org/10.1049/cvi2.12240 |
Similar Items
-
Continuous word level sign language recognition using an expert system based on machine learning
by: R Sreemathy, et al.
Published: (2023-06-01) -
Perspective and Evolution of Gesture Recognition for Sign Language: A Review
by: Jesús Galván-Ruiz, et al.
Published: (2020-06-01) -
Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks
by: Norah Alnaim, et al.
Published: (2020-04-01) -
Enhancing sign language recognition using CNN and SIFT: A case study on Pakistan sign language
by: Sadia Arooj, et al.
Published: (2024-02-01) -
UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals
by: Yuejiao Wang, et al.
Published: (2023-02-01)