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John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Folly Beach Library
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Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
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Phone: (843) 883-3914
Wando Mount Pleasant Library
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Phone: (843) 805-6888
Village Library
9 a.m. – 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
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Otranto Road Library
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McClellanville Library
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Phone: (843) 805-6930
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Generative innovations for paleography: enhancing character image synthesis through unconditional single image models.
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- Author(s): Aswathy, A.; Maheswari, P. Uma
- Source:
Heritage Science; 7/26/2024, Vol. 12 Issue 1, p1-12, 12p- Subject Terms:
- Source:
- Additional Information
- Abstract: Data scarcity in paleographic image datasets poses a significant challenge to researchers and scholars in the field. Unlike modern printed texts, historical manuscripts and documents are often scarce and fragile, making them difficult to digitize and create comprehensive datasets. Recently many innovations have been arrived on single image generative models for natural images but none of them are focused on paleographic character images and other handwritten datasets. In paleographic images like stone inscription characters, maintaining exact shape and structure of character is important unlike natural images. In this paper we propose an unconditional single image generative model, CharGAN for isolated paleographic character images. In the proposed system, augmented images are generated from a single image using generative adversarial networks, while maintaining their structure. Specifically, an external augmentation inducer is used to create higher-level augmentations in the generated images. In addition, the input to the generator is replaced with dynamic sampling from a Gaussian mixture model to make changes to the low-level features. From our experimental results, we infer that these two enhancements make single-image generative models suitable not only for natural images, but also for paleographic character images and other handwritten character datasets, the AHCD dataset, and EMNIST, where the global structure is important. Both the qualitative and quantitative results show that our approach is effective and superior in single-image generative tasks, particularly in isolated character image generation. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Heritage Science is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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