Comparison of Gaussian Hidden Markov Model and Convolutional Neural Network in Sign Language Recognition System

2021 ◽  
Author(s):  
Herman Gunawan ◽  
Suharjito ◽  
Devriady Pratama
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Hidden Markov ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition

scholarly journals A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets

TEM Journal ◽  
2020 ◽  
pp. 937-943
Author(s):  
Rasha Amer Kadhim ◽  
Muntadher Khamees
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Real Time ◽  
Sign Language ◽  
Recognition System ◽  
High Accuracy ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

In this paper, a real-time ASL recognition system was built with a ConvNet algorithm using real colouring images from a PC camera. The model is the first ASL recognition model to categorize a total of 26 letters, including (J & Z), with two new classes for space and delete, which was explored with new datasets. It was built to contain a wide diversity of attributes like different lightings, skin tones, backgrounds, and a wide variety of situations. The experimental results achieved a high accuracy of about 98.53% for the training and 98.84% for the validation. As well, the system displayed a high accuracy for all the datasets when new test data, which had not been used in the training, were introduced.

Sign in / Sign up

Export Citation Format

Share Document