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A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets.
- Source :
-
TEM Journal . Aug2020, Vol. 9 Issue 3, p937-943. 7p. - Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 22178309
- Volume :
- 9
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- TEM Journal
- Publication Type :
- Academic Journal
- Accession number :
- 145705051
- Full Text :
- https://doi.org/10.18421/TEM93-14