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Gesture Recognition for Interpretation of Bengali Sign Language using Hyper Parameter Tuning Convolution Neural Network

by Tahmina Akter, Muhammad Anwarul Azim, Mohammad Khairul Islam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 13
Year of Publication: 2023
Authors: Tahmina Akter, Muhammad Anwarul Azim, Mohammad Khairul Islam
10.5120/ijca2023922672

Tahmina Akter, Muhammad Anwarul Azim, Mohammad Khairul Islam . Gesture Recognition for Interpretation of Bengali Sign Language using Hyper Parameter Tuning Convolution Neural Network. International Journal of Computer Applications. 185, 13 ( Jun 2023), 1-7. DOI=10.5120/ijca2023922672

@article{ 10.5120/ijca2023922672,
author = { Tahmina Akter, Muhammad Anwarul Azim, Mohammad Khairul Islam },
title = { Gesture Recognition for Interpretation of Bengali Sign Language using Hyper Parameter Tuning Convolution Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 13 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number13/32753-2023922672/ },
doi = { 10.5120/ijca2023922672 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:56.014425+05:30
%A Tahmina Akter
%A Muhammad Anwarul Azim
%A Mohammad Khairul Islam
%T Gesture Recognition for Interpretation of Bengali Sign Language using Hyper Parameter Tuning Convolution Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 13
%P 1-7
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, the proportion of deaf and dumb persons has increased dramatically all across the world. Physically challenged persons have found it challenging to communicate with normal others. Via the movement of gestures like the face, hand, and body, communication takes place. The goal of this research is to develop a hand gesture recognition method that can understand sign language for deaf and hard of hearing persons. We collected our hand sign data from a well-known source called Ishara- Lipi. Because of the low amount of gestures, we augment this data set from 1009 images to 9360 images for our recognition purposes. This augmented dataset is divided into the following sections: training and testing. We develop the Convolutional Neural Network (CNN) model that uses multilayer CNN, followed by pooling layers and dropout layers and also adding multiple hidden layers, or simply called dense layers afterward. We experiment with our CNN model with the tuning of hyper parameters in all possible combinations. Our model provides 98.78 percent and 97.45 percent accuracy in training and validation respectively. We evaluate our trained CNN model through the test dataset that also has provided both 98 percent for accuracy and f1 score.

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Index Terms

Computer Science
Information Sciences

Keywords

Gesture recognition sign language CNN hyper parameter tuning.

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