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Stone inscription image segmentation based on Stacked-UNets and GANs
- Source :
- Discover Applied Sciences, Vol 6, Iss 10, Pp 1-15 (2024)
- Publication Year :
- 2024
- Publisher :
- Springer, 2024.
-
Abstract
- Abstract To overcome the challenges posed in effectively extracting stone inscriptions characterized by highly self-similarity between the foreground and background, a character image segmentation framework is proposed that integrates Stacked-UNets and Generative Adversarial Networks (GANs). Initially, a convolutional rule tailored for self-similar feature extraction is introduced to enhance the image detail segmentation. Subsequently, in improving the stacked units, multi-scale character masks with Spatial Transformer Network (STN) are added to guide the character segmentation. Finally, with two GANs and datasets, the character recognition and generation capabilities of the Stacked-UNets are trained, and further constructing its character segmentation and restoration abilities. Ultimately, the edge segmentation performance was enhanced, both in extracting characters from stone inscriptions with self-similar blocks and in effectively recovering partially incomplete characters. Compared to state-of-the-art methods, the Stacked-UNets demonstrates improvements of 6.41%, 7.39%, 8.43%, 8.11%, 4.13%, and 2.58% across six indicators, respectively.
Details
- Language :
- English
- ISSN :
- 30049261
- Volume :
- 6
- Issue :
- 10
- Database :
- Directory of Open Access Journals
- Journal :
- Discover Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.666af7173ddf49e3b5770d2b5c3e4a5f
- Document Type :
- article
- Full Text :
- https://doi.org/10.1007/s42452-024-06264-8