Object Character Recognition for automatic labelling of pharmaceutical products

In the current modern era, storing data information from images or documents to a computer drive is in high demand as it can be utilized the information for various purposes, especially in the pharmaceutical industry. The current method of storing data information about pharmaceutical products is to...

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Main Author:	Abdul Rahman, Muhammad Hanafi Akmal
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99589/1/MuhammadHanafiAkmalMSKE2022.pdf
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id	my-utm-ep.99589
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spelling	my-utm-ep.995892023-03-08T03:35:44Z Object Character Recognition for automatic labelling of pharmaceutical products 2022 Abdul Rahman, Muhammad Hanafi Akmal TK Electrical engineering. Electronics Nuclear engineering In the current modern era, storing data information from images or documents to a computer drive is in high demand as it can be utilized the information for various purposes, especially in the pharmaceutical industry. The current method of storing data information about pharmaceutical products is to manually key-in the information about the products to the computer system. Therefore, one simple method for storing information from documents on a computer system would be to scan the image or document and then save it as an image file. However, analysing this information from the image can be exceedingly difficult. There is a need for dependable manual labour to review the information on pharmaceutical products. For this reason, a method to automatically fetch and store the information from the image is required. Object Character Recognition (OCR) is a well-known method that can identify and process information from pixel-based images to text format. In this thesis, OCR is implemented to extract text characters from images for the labelling of pharmaceutical products. The challenges that are associated with this task include variances in illumination, rotation when acquiring the image, and the different fonts that are shown on the pharmaceutical product. Besides, there is too much information for the computer system to accurately retrieve from the images. In addition, Named Entity Recognition (NER) is implemented to identify the important information from the OCR process. The system successfully extracts all the important information for several pharmaceutical products and successfully converts them into a sample form. The results obtained by OCR show a 92.85% accuracy rate. Meanwhile, the results obtained by NER have a 100% accuracy rate for MAL numbers and a 90% accuracy rate for product names. Overall, it is hoped that this system may help to optimize the work in the pharmaceutical supply chain industry and contribute towards the national industry. 2022 Thesis http://eprints.utm.my/id/eprint/99589/ http://eprints.utm.my/id/eprint/99589/1/MuhammadHanafiAkmalMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149951 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Abdul Rahman, Muhammad Hanafi Akmal Object Character Recognition for automatic labelling of pharmaceutical products
description	In the current modern era, storing data information from images or documents to a computer drive is in high demand as it can be utilized the information for various purposes, especially in the pharmaceutical industry. The current method of storing data information about pharmaceutical products is to manually key-in the information about the products to the computer system. Therefore, one simple method for storing information from documents on a computer system would be to scan the image or document and then save it as an image file. However, analysing this information from the image can be exceedingly difficult. There is a need for dependable manual labour to review the information on pharmaceutical products. For this reason, a method to automatically fetch and store the information from the image is required. Object Character Recognition (OCR) is a well-known method that can identify and process information from pixel-based images to text format. In this thesis, OCR is implemented to extract text characters from images for the labelling of pharmaceutical products. The challenges that are associated with this task include variances in illumination, rotation when acquiring the image, and the different fonts that are shown on the pharmaceutical product. Besides, there is too much information for the computer system to accurately retrieve from the images. In addition, Named Entity Recognition (NER) is implemented to identify the important information from the OCR process. The system successfully extracts all the important information for several pharmaceutical products and successfully converts them into a sample form. The results obtained by OCR show a 92.85% accuracy rate. Meanwhile, the results obtained by NER have a 100% accuracy rate for MAL numbers and a 90% accuracy rate for product names. Overall, it is hoped that this system may help to optimize the work in the pharmaceutical supply chain industry and contribute towards the national industry.
format	Thesis
qualification_level	Master's degree
author	Abdul Rahman, Muhammad Hanafi Akmal
author_facet	Abdul Rahman, Muhammad Hanafi Akmal
author_sort	Abdul Rahman, Muhammad Hanafi Akmal
title	Object Character Recognition for automatic labelling of pharmaceutical products
title_short	Object Character Recognition for automatic labelling of pharmaceutical products
title_full	Object Character Recognition for automatic labelling of pharmaceutical products
title_fullStr	Object Character Recognition for automatic labelling of pharmaceutical products
title_full_unstemmed	Object Character Recognition for automatic labelling of pharmaceutical products
title_sort	object character recognition for automatic labelling of pharmaceutical products
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99589/1/MuhammadHanafiAkmalMSKE2022.pdf
_version_	1776100623439101952

Object Character Recognition for automatic labelling of pharmaceutical products

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