This article surveys ongoing research of the Legibility Enhancement of Ostraca (LEO) team of Tel Aviv University in the field of computerized paleography of Hebrew Iron Age ink-written ostraca. We perform paleographic tasks using tools from the fields of image processing and machine learning. Several new techniques serving this aim, as well as an adaptation of existing ones, are described herein. This includes testing a range of signal-acquisition methodologies, out of which multispectral imaging and Raman spectroscopy have matured into imaging systems. In addition, we deal with semior fully automated facsimile construction and refinement, facsimile, and character evaluation, as well as the reconstruction of broken character strokes. We conclude with future research directions, addressing some of the long-standing epigraphic questions, such as the number of scribes in specific corpora or detection of chronological concurrences and inconsistencies.