A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
Author(s): Rasha Amer Kadhim, Muntadher KhameesSubject(s): ICT Information and Communications Technologies
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: ASL recognition system; deep learning; convolutional neural network (CNNs); classification; real-time;
Summary/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.
Journal: TEM Journal
- Issue Year: 9/2020
- Issue No: 3
- Page Range: 937-943
- Page Count: 7
- Language: English
