Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli

For the past few decades, braille is one of the tools that are being used to help visually impaired people to engage with the world. Braille is the most popular system used for interaction between visually-impaired and sighted people using tactile means. As the numbers of people with vision impairme...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohamad Zulfadhli, Nurul Ain Ellisa
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96449/1/96449.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.96449
record_format	uketd_dc
spelling	my-uitm-ir.964492024-06-05T23:35:36Z Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli 2024 Mohamad Zulfadhli, Nurul Ain Ellisa Neural networks (Computer science) For the past few decades, braille is one of the tools that are being used to help visually impaired people to engage with the world. Braille is the most popular system used for interaction between visually-impaired and sighted people using tactile means. As the numbers of people with vision impairment are growing over the years, they also need a system that can aid their impairment. The main purpose of this project is to develop and evaluate a prototype of braille characters’ recognition that is able to identify Grade 1 and Grade 2 braille characters. This study develops a CNNbased system to recognize Braille characters, addressing the translation challenges faced by people with vision impairments. The dataset comprises 28x28 black and white images of 26 characters, each with three augmentations, sourced from Kaggle. CNNs analyze dot patterns for classification. Target users, particularly Braille instructors, benefit from this learning aid, enhancing accessibility and inclusivity for visually impaired individuals. Therefore, Convolutional Neural Network (CNN) technique is used to construct a model that is able to identify the braille characters. Two experiments were conducted on the number of epochs and splitting data ratios. Based on the results, the most outstanding model achieved 97.1% accuracy with the 600 number of epochs. The future works for this prototype system are to develop a mobile application or web-based application to identify the braille characters and translate the characters. Besides, another recommendation is to add another braille characters along with symbols for the system to identify. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96449/ https://ir.uitm.edu.my/id/eprint/96449/1/96449.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Abu Bakar, Nor Fauziah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Abu Bakar, Nor Fauziah
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Mohamad Zulfadhli, Nurul Ain Ellisa Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli
description	For the past few decades, braille is one of the tools that are being used to help visually impaired people to engage with the world. Braille is the most popular system used for interaction between visually-impaired and sighted people using tactile means. As the numbers of people with vision impairment are growing over the years, they also need a system that can aid their impairment. The main purpose of this project is to develop and evaluate a prototype of braille characters’ recognition that is able to identify Grade 1 and Grade 2 braille characters. This study develops a CNNbased system to recognize Braille characters, addressing the translation challenges faced by people with vision impairments. The dataset comprises 28x28 black and white images of 26 characters, each with three augmentations, sourced from Kaggle. CNNs analyze dot patterns for classification. Target users, particularly Braille instructors, benefit from this learning aid, enhancing accessibility and inclusivity for visually impaired individuals. Therefore, Convolutional Neural Network (CNN) technique is used to construct a model that is able to identify the braille characters. Two experiments were conducted on the number of epochs and splitting data ratios. Based on the results, the most outstanding model achieved 97.1% accuracy with the 600 number of epochs. The future works for this prototype system are to develop a mobile application or web-based application to identify the braille characters and translate the characters. Besides, another recommendation is to add another braille characters along with symbols for the system to identify.
format	Thesis
qualification_level	Bachelor degree
author	Mohamad Zulfadhli, Nurul Ain Ellisa
author_facet	Mohamad Zulfadhli, Nurul Ain Ellisa
author_sort	Mohamad Zulfadhli, Nurul Ain Ellisa
title	Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli
title_short	Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli
title_full	Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli
title_fullStr	Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli
title_full_unstemmed	Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli
title_sort	braille characters’ recognition using convolutional neural network / nurul ain ellisa mohamad zulfadhli
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96449/1/96449.pdf
_version_	1804889989296685056

Braille characters’ recognition using Convolutional Neural Network / Nurul Ain Ellisa Mohamad Zulfadhli

Similar Items