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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Mathematical Sciences

Committee Chair

Ionut Iacob

Committee Member 1

Yan Wu

Committee Member 2

Scott Kersey


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.

Research Data and Supplementary Material


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