Term of Award
Spring 2024
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Ionut Iacob
Committee Member 1
Yan Wu
Committee Member 2
Scott Kersey
Abstract
The Generative Adversarial Networks (GAN) recently emerged as a powerful framework for producing new knowledge from existing knowledge. These models aim to learn patterns from input data then use that knowledge to generate output data samples that plausibly appear to belong to the same set as the input data. Medieval manuscripts study has been an important research area in the humanities field for many decades. These rare manuscripts are often times inaccessible to the general public, including students in scholars, and it is of a great interest to provide digital support (including, but not limited to translation and search) for accessing these materials. We propose a GAN framework that uses manuscript images and their translations to create a model capable of new translation from new manuscript images. Such a model would provide great assistance to humanities researchers seeking to produce digital editions of old manuscripts.
Recommended Citation
Holtz, Tonilynn M., "Bringing GANs to Medieval Times: Manuscript Translation Models" (2024). Electronic Theses and Dissertations. 2741.
https://digitalcommons.georgiasouthern.edu/etd/2741
Research Data and Supplementary Material
No
