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Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System

Character recognition system based on human inspection is unpractical due to lack of accuracy and high cost. Therefore, investigating on automated character inspection system by computer is needed to improve the accuracy, reduce the cost and inspection time. In this project, a Beagle Bone Black (BBB...

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Main Authors: Chong, Wei Jian, M. Z., Ibrahim, Thum, Wei Seong, Ting, Ei Wei, Sabira, Khatun
Format: Article
Language:English
Published: UTeM 2017
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/19731/1/Embedded%20Character%20Recognition%20System%20using%20Random%20Forest%20Algorithm%20for%20IC%20Inspection%20System.pdf
http://umpir.ump.edu.my/id/eprint/19731/
http://journal.utem.edu.my/index.php/jtec/article/view/3498
http://journal.utem.edu.my/index.php/jtec/article/view/3498
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spelling my.ump.umpir.197312020-01-30T07:18:07Z http://umpir.ump.edu.my/id/eprint/19731/ Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System Chong, Wei Jian M. Z., Ibrahim Thum, Wei Seong Ting, Ei Wei Sabira, Khatun TK Electrical engineering. Electronics Nuclear engineering Character recognition system based on human inspection is unpractical due to lack of accuracy and high cost. Therefore, investigating on automated character inspection system by computer is needed to improve the accuracy, reduce the cost and inspection time. In this project, a Beagle Bone Black (BBB) was used as a processing device and Logitech webcam was used for as an image acquisition device. Total of 1080 training samples will undergo the image pre-processing, character segmentation, feature extraction and training using random forest classifier. The optimal parameter values of random forest classifier are determined by computing cross validation misclassification rate. The maximum number of splits, number of trees, and learning rate that yields the zero-misclassification rate is 1, 39 and 0.10 respectively. The process of testing random forest classifier was done using SN74LS27N chip under five different illuminations: no LED, one LED, two LED, three LED and four LED. From the experiments, it shows that the proposed system able to achieve 90.00% of accuracy within 1second to recognize characters on the SN74LS27N chip compared to 65.56% accuracy of human inspection. UTeM 2017 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19731/1/Embedded%20Character%20Recognition%20System%20using%20Random%20Forest%20Algorithm%20for%20IC%20Inspection%20System.pdf Chong, Wei Jian and M. Z., Ibrahim and Thum, Wei Seong and Ting, Ei Wei and Sabira, Khatun (2017) Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System. Journal of Telecommunication, Electronic and Computer Engineering, 10 (1-3). pp. 121-125. ISSN 2289-8131 (Unpublished) http://journal.utem.edu.my/index.php/jtec/article/view/3498 http://journal.utem.edu.my/index.php/jtec/article/view/3498
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Chong, Wei Jian
M. Z., Ibrahim
Thum, Wei Seong
Ting, Ei Wei
Sabira, Khatun
Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System
description Character recognition system based on human inspection is unpractical due to lack of accuracy and high cost. Therefore, investigating on automated character inspection system by computer is needed to improve the accuracy, reduce the cost and inspection time. In this project, a Beagle Bone Black (BBB) was used as a processing device and Logitech webcam was used for as an image acquisition device. Total of 1080 training samples will undergo the image pre-processing, character segmentation, feature extraction and training using random forest classifier. The optimal parameter values of random forest classifier are determined by computing cross validation misclassification rate. The maximum number of splits, number of trees, and learning rate that yields the zero-misclassification rate is 1, 39 and 0.10 respectively. The process of testing random forest classifier was done using SN74LS27N chip under five different illuminations: no LED, one LED, two LED, three LED and four LED. From the experiments, it shows that the proposed system able to achieve 90.00% of accuracy within 1second to recognize characters on the SN74LS27N chip compared to 65.56% accuracy of human inspection.
format Article
author Chong, Wei Jian
M. Z., Ibrahim
Thum, Wei Seong
Ting, Ei Wei
Sabira, Khatun
author_facet Chong, Wei Jian
M. Z., Ibrahim
Thum, Wei Seong
Ting, Ei Wei
Sabira, Khatun
author_sort Chong, Wei Jian
title Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System
title_short Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System
title_full Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System
title_fullStr Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System
title_full_unstemmed Embedded Character Recognition System using Random Forest Algorithm for IC Inspection System
title_sort embedded character recognition system using random forest algorithm for ic inspection system
publisher UTeM
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/19731/1/Embedded%20Character%20Recognition%20System%20using%20Random%20Forest%20Algorithm%20for%20IC%20Inspection%20System.pdf
http://umpir.ump.edu.my/id/eprint/19731/
http://journal.utem.edu.my/index.php/jtec/article/view/3498
http://journal.utem.edu.my/index.php/jtec/article/view/3498
_version_ 1657487805425647616
score 13.211869

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