Author:
Amer Kadhim Rasha,Khamees Muntadher
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.
Publisher
Association for Information Communication Technology Education and Science (UIKTEN)
Subject
Management of Technology and Innovation,Information Systems and Management,Strategy and Management,Education,Information Systems,Computer Science (miscellaneous)
Cited by
22 articles.
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