Ancient Javanese Manuscript Reconstruction Using Generative Adversarial Network with StarGAN v2 Variations - Kukuh Cokro Wibowo; Fitri Damayanti; Fanky Abdilqoyyim (Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya, 2025)
Ancient Javanese manuscripts are part of Indonesia's cultural heritage; most of them are usually in bad condition due to the age and environmental surroundings. This paper presents a manuscript reconstruction using the Generative Adversarial Network model, using the variation of StarGAN v2. The primary objective of this research is to assist philologists in reconstructing damaged manuscripts more efficiently, reducing the time and effort compared to manual reconstruction methods. The training for 100 epochs is performed by the model in order to generate the reconstruction image closest to ground truth. This study is done on a dataset that consists of a set of damaged manuscript images. In this dataset, 80% is for training, 20% is for validation, and 10 images are used for testing. Quality assessment will be made on image outputs during training, based on PSNR, SSIM, and LPIPS metrics. The results indicate that the PSNR increases from 16.1234 dB at the 50th epoch to 17.5588 dB at the 100th epoch, while the SSIM increases from 0.8374 to 0.8519, showing a strong improvement in image quality. Despite the LPIPS having a very slight increase from 0.1020 to 0.1051, this evidences that the model can be further improved. Overall, this study demonstrates that the StarGAN v2 model is effective in reconstructing ancient Javanese manuscripts-a great contribution to the field of cultural heritage preservation using modern technology.