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Open AccessArticle
Advancing Ancient Artifact Character Image Augmentation through Styleformer-ART for Sustainable Knowledge Preservation
by
Jamiu T. Suleiman
Jamiu T. Suleiman and
Im Y. Jung
Im Y. Jung *
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6455; https://doi.org/10.3390/su16156455 (registering DOI)
Submission received: 2 June 2024
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Revised: 11 July 2024
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Accepted: 24 July 2024
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Published: 28 July 2024
Abstract
The accurate detection of ancient artifacts is very crucial in recognizing and tracking the origin of these relics. The methodologies used in engraving characters onto these objects are different from the ones used in the modern era, prompting the need to develop tools that are accurately tailored to detect these characters. The challenge encountered in developing an object character recognition model for this purpose is the lack of sufficient data needed to train these models. In this work, we propose Styleformer-ART to augment the ancient artifact character images. To show the performance of Styleformer-ART, we compared Styleformer-ART with different state-of-the-art data augmentation techniques. To make a conclusion on the best augmentation method for this special dataset, we evaluated all the augmentation methods employed in this work using the Frétchet inception distance (FID) score between the reference images and the generated images. The methods were also evaluated on the recognition accuracy of a CNN model. The Styleformer-ART model achieved the best FID score of 210.72, and Styleformer-ART-generated images achieved a recognition accuracy with the CNN model of 84%, which is better than all the other reviewed image-generation models.
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MDPI and ACS Style
Suleiman, J.T.; Jung, I.Y.
Advancing Ancient Artifact Character Image Augmentation through Styleformer-ART for Sustainable Knowledge Preservation. Sustainability 2024, 16, 6455.
https://doi.org/10.3390/su16156455
AMA Style
Suleiman JT, Jung IY.
Advancing Ancient Artifact Character Image Augmentation through Styleformer-ART for Sustainable Knowledge Preservation. Sustainability. 2024; 16(15):6455.
https://doi.org/10.3390/su16156455
Chicago/Turabian Style
Suleiman, Jamiu T., and Im Y. Jung.
2024. "Advancing Ancient Artifact Character Image Augmentation through Styleformer-ART for Sustainable Knowledge Preservation" Sustainability 16, no. 15: 6455.
https://doi.org/10.3390/su16156455
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