Abstract

Historical palimpsests are manuscripts that have more than one layer of text, where the original text was erased, and subsequent layers overwritten. For many centuries, palimpsested text of ancient manuscripts was considered impossible to recover. Nowadays, with new technology, such as multispectral imaging, under some portion of electromagnetic spectrum illumination, the overwritten text can become visible again. This peculiar property of faded ink and some traditional signal processing methods had already helped recover text from numerous manuscripts. Nevertheless, even showing the best performance, existing methods can only enhance the visibility of the text but not eliminate the effects of occlusion from the overlapping text, stains, and fading of aged parchment. The dependency of the performance of these methods on material variation caused by aging, ink composition, and the text-erasing process, is not well understood. This factors makes their performance very inconsistent and parameter tuning very labor-intensive. As a solutions, we suggest reconstructing the palimpsest text using deep neural networks that have already proved to be the leading technology in the field of image reconstruction, in-painting, and super-resolution. Additionally, we present a method that combines spectral information from a multispectral imaging system with spatial statistics of palimpsest text.

Library of Congress Subject Headings

Optical character recognition; Palimpsests--Imaging; Deep learning (Machine learning); Multispectral imaging

Publication Date

8-2023

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science

College

College of Science

Advisor

David W. Messinger

Advisor/Committee Member

Cecilia Ovesdotter Alm

Advisor/Committee Member

Nathan Cahill

Campus

RIT – Main Campus

Plan Codes

IMGS-PHD

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