Computationally Accelerated Papyrology

Abstract

Papyrologists transcribe and identify papyrus fragments in order to enrich modern lives by better understanding the linguistics, culture, and literature of the ancient world. In practice, these tasks are extremely challenging and slow due the limited amount of information preserved in each papyrus fragment (i.e., due to deterioration). For example, since their discovery in the late 19th century, only 10\% of the more than 500,000 fragments in the Oxyrhynchus papyri collection has been given preliminary identifications.

This thesis presents two computational approaches for accelerating papyrus transcription and identification. The first approach is a computational pipeline that aggregates millions of crowdsourced letter classifications into transcriptions of papyrus fragments. The second approach leverages genetic sequence alignment algorithms to rapidly identify damaged papyrus fragments to known papyrus manuscripts. These approaches greatly improve upon the current state-of-the-art techniques and set a new standard for leveraging computation to the transcription and identification of ancient texts.