Deep learning-based isolated sign language recognition: a novel approach to tackling communication barriers for individuals with hearing impairments

Naciye Nur Arslan; Emrullah Şahin; Muammer Akçay

Article Details

Deep learning-based isolated sign language recognition: a novel approach to tackling communication barriers for individuals with hearing impairments

Authors:

Naciye Nur Arslan | Emrullah Şahin | Muammer Akçay

Publisher:

Kütahya Dumlupinar University

Main Topics:

Deep Learning

Journal:

Journal of Scientific Reports-A Issue 055, pp.50-59

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001299699

pISSN:

2687-6167

Keywords:

Abstract:

 Sign language is a primary and widely used means of communication for individuals with hearing impairments. Current sign language recognition techniques need to be improved and need further development. In this research, we present a novel deep learning architecture for achieving significant advancements in sign language recognition by recognizing isolated signs. The study utilizes the Isolated Sign Language Recognition ISLR dataset from 21 hard-of-hearing participants. This dataset comprises 250 isolated signs and the x, y, and z coordinates of 543 hand gestures obtained using MediaPipe Holistic Solution. With approximately 100,000 videos, this dataset presents an essential opportunity for applying deep learning methods in sign language recognition. We present the comparative results of our experiments, where we explored different batch sizes, kernel sizes, frame sizes, and different convolutional layers. We achieve an accuracy rate of 83.32 on the test set.

 Sign language is a primary and widely used means of communication for individuals with hearing impairments. Current sign language recognition techniques need to be improved and need further development. In this research, we present a novel deep learning architecture for achieving significant advancements in sign language recognition by recognizing isolated signs. The study utilizes the Isolated Sign Language Recognition ISLR dataset from 21 hard-of-hearing participants. This dataset comprises 250 isolated signs and the x, y, and z coordinates of 543 hand gestures obtained using MediaPipe Holistic Solution. With approximately 100,000 videos, this dataset presents an essential opportunity for applying deep learning methods in sign language recognition. We present the comparative results of our experiments, where we explored different batch sizes, kernel sizes, frame sizes, and different convolutional layers. We achieve an accuracy rate of 83.32 on the test set.

 Sign language is a primary and widely used means of communication for individuals with hearing impairments. Current sign language recognition techniques need to be improved and need further development. In this research, we present a novel deep learning architecture for achieving significant advancements in sign language recognition by recognizing isolated signs. The study utilizes the Isolated Sign Language Recognition ISLR dataset from 21 hard-of-hearing participants. This dataset comprises 250 isolated signs and the x, y, and z coordinates of 543 hand gestures obtained using MediaPipe Holistic Solution. With approximately 100,000 videos, this dataset presents an essential opportunity for applying deep learning methods in sign language recognition. We present the comparative results of our experiments, where we explored different batch sizes, kernel sizes, frame sizes, and different convolutional layers. We achieve an accuracy rate of 83.32 on the test set.

 Sign language is a primary and widely used means of communication for individuals with hearing impairments. Current sign language recognition techniques need to be improved and need further development. In this research, we present a novel deep learning architecture for achieving significant advancements in sign language recognition by recognizing isolated signs. The study utilizes the Isolated Sign Language Recognition ISLR dataset from 21 hard-of-hearing participants. This dataset comprises 250 isolated signs and the x, y, and z coordinates of 543 hand gestures obtained using MediaPipe Holistic Solution. With approximately 100,000 videos, this dataset presents an essential opportunity for applying deep learning methods in sign language recognition. We present the comparative results of our experiments, where we explored different batch sizes, kernel sizes, frame sizes, and different convolutional layers. We achieve an accuracy rate of 83.32 on the test set.

Article Details

Deep learning-based isolated sign language recognition: a novel approach to tackling communication barriers for individuals with hearing impairments

Authors:

Naciye Nur Arslan | Emrullah Şahin | Muammer Akçay

Publisher:

Kütahya Dumlupinar University

Main Topics:

Deep Learning

Journal:

Journal of Scientific Reports-A Issue 055, pp.50-59

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001299699

pISSN:

2687-6167

sign language recognition

deep learning

landmarks

ISLR

Issues

Article Details

Deep learning-based isolated sign language recognition: a novel approach to tackling communication barriers for individuals with hearing impairments

Authors: Naciye Nur Arslan | Emrullah Şahin | Muammer Akçay

Publisher: Kütahya Dumlupinar University

Main Topics: Deep Learning

Journal: Journal of Scientific Reports-A Issue 055, pp.50-59

Document Type: Article

Publication Date: 12/2023

Document Language: English

Document ID (Org No.): 2001299699

pISSN: 2687-6167

sign language recognition

deep learning

landmarks

ISLR

Issues

Authors:

Naciye Nur Arslan | Emrullah Şahin | Muammer Akçay

Publisher:

Kütahya Dumlupinar University

Main Topics:

Deep Learning

Journal:

Journal of Scientific Reports-A Issue 055, pp.50-59

Document Type:

Article

Publication Date:

12/2023

Document Language:

English

Document ID (Org No.):

2001299699

pISSN:

2687-6167