Rotation, Translation and Scale Invariant Sign Word Recognition Using Deep Learning-Reference-Cited by-同舟云学术

Rotation, Translation and Scale Invariant Sign Word Recognition Using Deep Learning

Published:2023 Issue:3 Volume:44 Page:2521-2536
ISSN:0267-6192
Container-title:Computer Systems Science and Engineering
language:en
Short-container-title:

Author:

Saleh Musa Miah Abu,Shin Jungpil,Al Mehedi Hasan Md.,Abdur Rahim Md,Okuyama Yuichi

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

General Computer Science,Theoretical Computer Science,Control and Systems Engineering

Link

https://www.techscience.com/csse/v44n3/49179/pdf

Reference42 articles.

1. Gesture-based human-robot interaction for human assistance in manufacturing;Neto;The International Journal of Advanced Manufacturing Technology,2019

2. The effectiveness of the game-based learning system for the improvement of American sign language using kinect;Kamnardsiri;Electronic Journal of e-Learning,2017

3. Recognition of American sign language gestures in virtual reality using leap motion;Vaitkevičius;Applied Sciences,2019

4. Non-touch sign word recognition based on dynamic hand gesture using hybrid segmentation and CNN feature fusion;Rahim;Applied Sciences,2019

5. A review of hand gesture and sign language recognition techniques;Cheok;International Journal of Machine Learning and Cybernetics,2019

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effective features extraction and selection for hand gesture recognition using sEMG signal;Multimedia Tools and Applications;2024-06-03

2. Wearable patterned single electrode-based triboelectric nanogenerator for peak-encoded interface;Nano Energy;2024-06

3. Spatial–temporal attention with graph and general neural network-based sign language recognition;Pattern Analysis and Applications;2024-04-04

4. Dynamic Japanese Sign Language Recognition Throw Hand Pose Estimation Using Effective Feature Extraction and Classification Approach;Sensors;2024-01-26

5. A Deep Bidirectional LSTM Model Enhanced by Transfer-Learning-Based Feature Extraction for Dynamic Human Activity Recognition;Applied Sciences;2024-01-10