Continuous American Sign Language Recognition Using Computer Vision And Deep Learning Technologies
Author:
Affiliation:
1. Sri Lanka Institute of Information Technology,Department of Computer Science & Software Engineering,Malabe,Sri Lanka
2. Sri Lanka Institute of Information Technology,Department of Computer Systems Engineering,Malabe,Sri Lanka
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9864330/9864331/09864539.pdf?arnumber=9864539
Reference25 articles.
1. Sign Language Recognition Using Modified Convolutional Neural Network Model
2. End-to-End Residual Neural Network with Data Augmentation for Sign Language Recognition
3. Understanding and Coding a ResNet in Keras;dwivedi;Towards Data Science,2019
4. Real-time American sign language recognition using desk and wearable computer based video
5. Image-based Arabic Sign Language recognition system;ralghonaim;International Journal of Advanced Computer Science and Applications,2018
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1. Computer vision-based hybrid efficient convolution for isolated dynamic sign language recognition;Neural Computing and Applications;2024-08-09
2. Comparing different neural networks for accurate sign language recognition: A comprehensive study;AIP Conference Proceedings;2024
3. A Media-Pipe Integrated Deep Learning Model for ISL (Alphabet) Recognition and Converting Text to Sound with Video Input;Communications in Computer and Information Science;2024
4. Sign2Pose: A Pose-Based Approach for Gloss Prediction Using a Transformer Model;Sensors;2023-03-06
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