A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
Year: 2020
Type: article
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.
Source: TEM Journal
DOI: 10.18421/tem93-14
Authors: Rasha Amer Kadhim, Muntadher Khamees
Institution: University of Diyala
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $647
OpenAlex ID: W3083176480