Abstract
Due to ancient origin, there are many incomplete characters in the unearthed Oracle Bone Inscriptions(OBI), which brings the great challenges to recognition and research. In recent years, image inpainting techniques have made remarkable progress. However, these models are unable to adapt to the unique font shape and complex text background of OBI. To meet these aforementioned challenges, we propose a two-stage method for restoring damaged OBI using Generative Adversarial Networks (GAN), which incorporates a dual discriminator structure to capture both global and local image information. In order to accurately restore the image structure and details, the spatial attention mechanism and a novel loss function are proposed. By feeding clear copies of existing OBI and various types of masks into the network, it learns to generate content for the missing regions. Experimental results demonstrate the effectiveness of our proposed method in completing OBI compared to several state-of-the-art techniques.- Anthology ID:
- 2024.ml4al-1.12
- Volume:
- Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Hybrid in Bangkok, Thailand and online
- Editors:
- John Pavlopoulos, Thea Sommerschield, Yannis Assael, Shai Gordin, Kyunghyun Cho, Marco Passarotti, Rachele Sprugnoli, Yudong Liu, Bin Li, Adam Anderson
- Venues:
- ML4AL | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 107–114
- Language:
- URL:
- https://aclanthology.org/2024.ml4al-1.12
- DOI:
- 10.18653/v1/2024.ml4al-1.12
- Cite (ACL):
- Shibin Wang, Wenjie Guo, Yubo Xu, Dong Liu, and Xueshan Li. 2024. Coarse-to-Fine Generative Model for Oracle Bone Inscriptions Inpainting. In Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024), pages 107–114, Hybrid in Bangkok, Thailand and online. Association for Computational Linguistics.
- Cite (Informal):
- Coarse-to-Fine Generative Model for Oracle Bone Inscriptions Inpainting (Wang et al., ML4AL-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.ml4al-1.12.pdf