Braille image recognition for beginners / Nor Azrin Tarmizi

Braille can be known as a tactile that consist of dots that is used by visually impaired people in reading and to communicate. However, braille text is a complicated tools that is not easily read by normal people. Braille pattern of each alphabets consists of its own pattern in which some of it does...

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Main Author:	Tarmizi, Nor Azrin
Format:	Student Project
Language:	English
Published:	Faculty of Computer and Mathematical Sciences 2017
Subjects:	Communication of computer science information Programming. Rule-based programming. Backtrack programming Application software
Online Access:	http://ir.uitm.edu.my/id/eprint/21373/ http://ir.uitm.edu.my/id/eprint/21373/1/TD_NOR%20AZRIN%20TARMIZI%20M%20CS%2017_5.pdf

id	uitm-21373
recordtype	eprints
spelling	uitm-213732018-10-25T01:40:23Z http://ir.uitm.edu.my/id/eprint/21373/ Braille image recognition for beginners / Nor Azrin Tarmizi Tarmizi, Nor Azrin Communication of computer science information Programming. Rule-based programming. Backtrack programming Application software Braille can be known as a tactile that consist of dots that is used by visually impaired people in reading and to communicate. However, braille text is a complicated tools that is not easily read by normal people. Braille pattern of each alphabets consists of its own pattern in which some of it does not relate to its alphabet. Thus, it is difficult for normal user to detect and recognize the braille pattern. Hence, the objective of this study is to design and develop a prototype that can translate braille image pattern into readable English text using image processing. In order to recognize the braille image, Bag of Features (BOF) is used for the recognition. BOF consists of Speeded-up Robust Features (SURF) and K-means Clustering. SURF technique determines and generates the features point description of each alphabet whereas K-means Clustering is to get the clusters of words in which it selects the most closer to its image. Additionally, image classification is done where it determines the alphabet of braille image using linear SVM classifier. In this study, 78 of braille images are collected from the internet. Out of 78 images, 76 images have been tested to produce result that is similar to the expected outcome. It is found that 97.44% of accuracy is achieved for overall testing data. From the result, it can be concluded that this study has meet the expectation where most of the braille images are recognizable. Faculty of Computer and Mathematical Sciences 2017 Student Project NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/21373/1/TD_NOR%20AZRIN%20TARMIZI%20M%20CS%2017_5.pdf Tarmizi, Nor Azrin (2017) Braille image recognition for beginners / Nor Azrin Tarmizi. [Student Project] (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Communication of computer science information Programming. Rule-based programming. Backtrack programming Application software
spellingShingle	Communication of computer science information Programming. Rule-based programming. Backtrack programming Application software Tarmizi, Nor Azrin Braille image recognition for beginners / Nor Azrin Tarmizi
description	Braille can be known as a tactile that consist of dots that is used by visually impaired people in reading and to communicate. However, braille text is a complicated tools that is not easily read by normal people. Braille pattern of each alphabets consists of its own pattern in which some of it does not relate to its alphabet. Thus, it is difficult for normal user to detect and recognize the braille pattern. Hence, the objective of this study is to design and develop a prototype that can translate braille image pattern into readable English text using image processing. In order to recognize the braille image, Bag of Features (BOF) is used for the recognition. BOF consists of Speeded-up Robust Features (SURF) and K-means Clustering. SURF technique determines and generates the features point description of each alphabet whereas K-means Clustering is to get the clusters of words in which it selects the most closer to its image. Additionally, image classification is done where it determines the alphabet of braille image using linear SVM classifier. In this study, 78 of braille images are collected from the internet. Out of 78 images, 76 images have been tested to produce result that is similar to the expected outcome. It is found that 97.44% of accuracy is achieved for overall testing data. From the result, it can be concluded that this study has meet the expectation where most of the braille images are recognizable.
format	Student Project
author	Tarmizi, Nor Azrin
author_facet	Tarmizi, Nor Azrin
author_sort	Tarmizi, Nor Azrin
title	Braille image recognition for beginners / Nor Azrin Tarmizi
title_short	Braille image recognition for beginners / Nor Azrin Tarmizi
title_full	Braille image recognition for beginners / Nor Azrin Tarmizi
title_fullStr	Braille image recognition for beginners / Nor Azrin Tarmizi
title_full_unstemmed	Braille image recognition for beginners / Nor Azrin Tarmizi
title_sort	braille image recognition for beginners / nor azrin tarmizi
publisher	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	http://ir.uitm.edu.my/id/eprint/21373/ http://ir.uitm.edu.my/id/eprint/21373/1/TD_NOR%20AZRIN%20TARMIZI%20M%20CS%2017_5.pdf
first_indexed	2023-09-18T23:06:24Z
last_indexed	2023-09-18T23:06:24Z
_version_	1777418512461987840

Braille image recognition for beginners / Nor Azrin Tarmizi

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