Handwriting analysis for personality trait features identification using CNN
Handwriting analysis is an approach to get information through the handwriting. It extremely useful information, for instance in personality traits identification. The information came from the feature extracted from the handwriting. This feature can be size, slantness, pressure, and so forth. In th...
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my.utm.989092023-02-08T05:18:43Z http://eprints.utm.my/id/eprint/98909/ Handwriting analysis for personality trait features identification using CNN Alamsyah, Derry Samsuryadi, Samsuryadi Widhiarsho, Wijang Hasan, Shafaatunnur QA75 Electronic computers. Computer science Handwriting analysis is an approach to get information through the handwriting. It extremely useful information, for instance in personality traits identification. The information came from the feature extracted from the handwriting. This feature can be size, slantness, pressure, and so forth. In this research, handwriting analysis is through the AND dataset that provide handwriting dataset along with feature label while most public dataset has nothing with it. By using the Coonvolutional Neural Networks (CNN) potentiality in capturing and recognizing global features, there are 15 models had built in this research in accordance with each feature and divided into three group by its number of types. After built a simple CNN architecture by only conduct two convolution layer, overall result show fair enough performance where the highest rate of accuracy is 80.88%. Furthermore, there are three best features had recognized, which is "entry stroke 'A'", "size", and "slantness", where the last two is naturally global features. However, the fact that handwriting image data cannot be oversampled which can lead to the bias result, than the imbalance data becomes a problem in this research that reduced the model performance. 2022 Conference or Workshop Item PeerReviewed Alamsyah, Derry and Samsuryadi, Samsuryadi and Widhiarsho, Wijang and Hasan, Shafaatunnur (2022) Handwriting analysis for personality trait features identification using CNN. In: 2022 International Conference on Data Science and Its Applications, ICoDSA 2022, 6 - 7 July 2022, Bandung, Indonesia. http://dx.doi.org/10.1109/ICoDSA55874.2022.9862910 |
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QA75 Electronic computers. Computer science Alamsyah, Derry Samsuryadi, Samsuryadi Widhiarsho, Wijang Hasan, Shafaatunnur Handwriting analysis for personality trait features identification using CNN |
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Handwriting analysis is an approach to get information through the handwriting. It extremely useful information, for instance in personality traits identification. The information came from the feature extracted from the handwriting. This feature can be size, slantness, pressure, and so forth. In this research, handwriting analysis is through the AND dataset that provide handwriting dataset along with feature label while most public dataset has nothing with it. By using the Coonvolutional Neural Networks (CNN) potentiality in capturing and recognizing global features, there are 15 models had built in this research in accordance with each feature and divided into three group by its number of types. After built a simple CNN architecture by only conduct two convolution layer, overall result show fair enough performance where the highest rate of accuracy is 80.88%. Furthermore, there are three best features had recognized, which is "entry stroke 'A'", "size", and "slantness", where the last two is naturally global features. However, the fact that handwriting image data cannot be oversampled which can lead to the bias result, than the imbalance data becomes a problem in this research that reduced the model performance. |
format |
Conference or Workshop Item |
author |
Alamsyah, Derry Samsuryadi, Samsuryadi Widhiarsho, Wijang Hasan, Shafaatunnur |
author_facet |
Alamsyah, Derry Samsuryadi, Samsuryadi Widhiarsho, Wijang Hasan, Shafaatunnur |
author_sort |
Alamsyah, Derry |
title |
Handwriting analysis for personality trait features identification using CNN |
title_short |
Handwriting analysis for personality trait features identification using CNN |
title_full |
Handwriting analysis for personality trait features identification using CNN |
title_fullStr |
Handwriting analysis for personality trait features identification using CNN |
title_full_unstemmed |
Handwriting analysis for personality trait features identification using CNN |
title_sort |
handwriting analysis for personality trait features identification using cnn |
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2022 |
url |
http://eprints.utm.my/id/eprint/98909/ http://dx.doi.org/10.1109/ICoDSA55874.2022.9862910 |
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13.19449 |