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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. This discovery grant will facilitate an important step towards the goal of meaningful automatic transcription and indexing of the extensive worldwide cuneiform tablet collection.