Malaysia currency recognizer mobile application for visual impairment

In Malaysia, the prevalence of blindness for all ages has an average of 1.2%. People with vision impairments have difficulty recognizing objects through a vision in daily life, especially for some principal daily used objects such as a banknote. The braille feature on top of the banknotes becomes un...

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Main Author: Lee, Kyai Lun
Format: Final Year Project / Dissertation / Thesis
Published: 2021
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Online Access:http://eprints.utar.edu.my/4289/1/17ACB01383_FYP.pdf
http://eprints.utar.edu.my/4289/
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spelling my-utar-eprints.42892022-01-05T08:13:36Z Malaysia currency recognizer mobile application for visual impairment Lee, Kyai Lun QA75 Electronic computers. Computer science T Technology (General) In Malaysia, the prevalence of blindness for all ages has an average of 1.2%. People with vision impairments have difficulty recognizing objects through a vision in daily life, especially for some principal daily used objects such as a banknote. The braille feature on top of the banknotes becomes unusable in its tactile form after a brief usage circulation. Nevertheless, the existing currency recognizer is inefficient and not able to identify Malaysia currency. In this project, a mobile application based on recognizing currency has been developed using deep learning transfer learning techniques. The pre-trained deep learning model, MobileNet V2 utilized to transfer learning on our detection model. One hundred images of each value of Malaysia currency were collected as the dataset for training the model. The output lightweight model from the training process deployed on the mobile application. The mobile application is designed with vision-friendly features embedded into the mobile application through Android studio. Embedded features include vibration notification, real-time scanning on a phone camera, and real-time result voice feedback. Our system reached a mean average accuracy of 97% and average inference time of 0.06 second on detecting the currency. In conclusion, we bring a Malaysia banknotes recognizer mobile application in more accessible and more accurate ways. 2021-07-19 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4289/1/17ACB01383_FYP.pdf Lee, Kyai Lun (2021) Malaysia currency recognizer mobile application for visual impairment. Final Year Project, UTAR. http://eprints.utar.edu.my/4289/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic QA75 Electronic computers. Computer science
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
Lee, Kyai Lun
Malaysia currency recognizer mobile application for visual impairment
description In Malaysia, the prevalence of blindness for all ages has an average of 1.2%. People with vision impairments have difficulty recognizing objects through a vision in daily life, especially for some principal daily used objects such as a banknote. The braille feature on top of the banknotes becomes unusable in its tactile form after a brief usage circulation. Nevertheless, the existing currency recognizer is inefficient and not able to identify Malaysia currency. In this project, a mobile application based on recognizing currency has been developed using deep learning transfer learning techniques. The pre-trained deep learning model, MobileNet V2 utilized to transfer learning on our detection model. One hundred images of each value of Malaysia currency were collected as the dataset for training the model. The output lightweight model from the training process deployed on the mobile application. The mobile application is designed with vision-friendly features embedded into the mobile application through Android studio. Embedded features include vibration notification, real-time scanning on a phone camera, and real-time result voice feedback. Our system reached a mean average accuracy of 97% and average inference time of 0.06 second on detecting the currency. In conclusion, we bring a Malaysia banknotes recognizer mobile application in more accessible and more accurate ways.
format Final Year Project / Dissertation / Thesis
author Lee, Kyai Lun
author_facet Lee, Kyai Lun
author_sort Lee, Kyai Lun
title Malaysia currency recognizer mobile application for visual impairment
title_short Malaysia currency recognizer mobile application for visual impairment
title_full Malaysia currency recognizer mobile application for visual impairment
title_fullStr Malaysia currency recognizer mobile application for visual impairment
title_full_unstemmed Malaysia currency recognizer mobile application for visual impairment
title_sort malaysia currency recognizer mobile application for visual impairment
publishDate 2021
url http://eprints.utar.edu.my/4289/1/17ACB01383_FYP.pdf
http://eprints.utar.edu.my/4289/
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score 13.211869