Vol.9, No.3, August 2020.                                                                                                                                                                                ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

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


Rasha Amer Kadhim, Muntadher Khamees


© 2020 Muntadher Khamees, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 9, Issue 3, Pages 937-943, ISSN 2217-8309, DOI: 10.18421/TEM93-14, August 2020.


Received: 05 March 2020.

Revised:   09 July 2020.
Accepted: 16 July 2020.
Published: 28 August 2020.




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.


Keywords –ASL recognition system, deep learning,convolutional neural network (CNNs), classification, real-time.



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