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

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 wi...

Full description

Bibliographic Details
Main Authors: Rasha Amer Kadhim, Muntadher Khamees
Format: Article
Language:English
Published: UIKTEN 2020-08-01
Series:TEM Journal
Subjects:
asl recognition system
deep learning
convolutional neural network (cnns)
classification
real-time
Online Access:http://www.temjournal.com/content/93/TEMJournalAugust_937_943.pdf
id doaj-71ebead6253d4b9ca0d49a1c1eee3b0f
record_format Article
spelling doaj-71ebead6253d4b9ca0d49a1c1eee3b0f2020-11-25T02:51:50ZengUIKTENTEM Journal2217-83092217-83332020-08-019393794310.18421/TEM93-14A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real DatasetsRasha Amer KadhimMuntadher KhameesIn 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.http://www.temjournal.com/content/93/TEMJournalAugust_937_943.pdfasl recognition systemdeep learningconvolutional neural network (cnns)classificationreal-time
collection DOAJ
language English
format Article
sources DOAJ
author Rasha Amer Kadhim
Muntadher Khamees
spellingShingle Rasha Amer Kadhim
Muntadher Khamees
A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
TEM Journal
asl recognition system
deep learning
convolutional neural network (cnns)
classification
real-time
author_facet Rasha Amer Kadhim
Muntadher Khamees
author_sort Rasha Amer Kadhim
title A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
title_short A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
title_full A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
title_fullStr A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
title_full_unstemmed A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets
title_sort real-time american sign language recognition system using convolutional neural network for real datasets
publisher UIKTEN
series TEM Journal
issn 2217-8309
2217-8333
publishDate 2020-08-01
description 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.
topic asl recognition system
deep learning
convolutional neural network (cnns)
classification
real-time
url http://www.temjournal.com/content/93/TEMJournalAugust_937_943.pdf
work_keys_str_mv AT rashaamerkadhim arealtimeamericansignlanguagerecognitionsystemusingconvolutionalneuralnetworkforrealdatasets
AT muntadherkhamees arealtimeamericansignlanguagerecognitionsystemusingconvolutionalneuralnetworkforrealdatasets
AT rashaamerkadhim realtimeamericansignlanguagerecognitionsystemusingconvolutionalneuralnetworkforrealdatasets
AT muntadherkhamees realtimeamericansignlanguagerecognitionsystemusingconvolutionalneuralnetworkforrealdatasets
_version_ 1724733065237889024

Similar Items