Khmer calligraphy style transfer using SkelGAN

Name: Chanarin Heng

Organization : Kasetsart University

Email : chanarin.h@ku.th

Name: Worasait Suwannik

Organization : Kasetsart University

Email : worasait.suwannik@gmail.com

keyword: Calligraphy, Khmer

LCSH: Optical character recognition

; Generative adversarial networks

LCSH: Machine learning

LCSH: Computer vision

LCSH: Image processing -- Digital techniques

Abstract: Font style transfer is a challenging task in computer vision, aimed at extracting the visual characteristics such as stroke contrast and apply them to the content image. In this article, we focus on utilizing SkelGAN, a modified version of the U-Net architecture, to transfer font styles from English to Khmer characters. For our experiment, we collected a dataset of 15 calligraphy fonts containing both Khmer and English characters. To assess the performance of the generated images, we used the Structural Similarity Index Measure (SSIM). We compare the generated images with the ground truth images, providing a quantitative assessment of their similarity. Our experimental results indicate that SkelGAN successfully performs style transfer from English calligraphy to Khmer calligraphy, achieving an average SSIM score of 0.93. This score signifies a significant resemblance between the generated and target styles

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2025-03-04

Issued: 2025-03-04

บทความ/Article

application/pdf

BibliograpyCitation : In King Mongkut's University of Technology Thonburi. The 13th International Conference on Advances in Information Technology (IAIT 2023) (Article 19). Bangkok : King Mongkut's University of Technology Thonburi

eng

©copyrights King Mongkut's University of Technology North Bangkok

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