Manually deciphering a cuneiform tablet is a laborious, time-consuming, and error-prone process. This project explores how recent advances in computer vision can assist researchers by automatically identifying symbols and words in images of cuneiform tablets. It will leverage the extensively annotated collections of the Online Cultural and Historical Research Environment (OCHRE) as training data for machine learning vision models that preliminary results suggest are accurate up to 83 percent of the time. The project is an important step towards the goal of meaningful automatic transcription and indexing of the extensive worldwide cuneiform tablet collection.
CDAC Data Dispatches Spring Series
During the spring quarter, the Center for Data and Computing will host a series of remote gatherings that feature ongoing CDAC-funded research and collaborations across units and fields. Each date will feature a CDAC project sharing brief presentations about their work, followed by open discussion between the researchers and the audience.
We encourage all CDAC research funding recipients to attend, and also invite faculty, students, and campus researchers interested in data science, machine learning, and other applied computational tools. Events will take place via Zoom software. Please RSVP if you plan to attend, and we will send updated links and reminders before the talk.