Most of the texts written in ancient Israel and Judah were ink on papyrus. But these primarily administrative and literary documents were perishable and did not survive the journey down the millennia. The only texts that endured the harsh local climate were the relatively few that were written in ink on pieces of pottery (ostraca). The largest groups of these ostraca were discovered in the excavations of Samaria, Lachish and Arad.


First Temple period (Iron Age II) epigraphy is an important component in the study of biblical history, ancient Hebrew and the biblical text. Traditionally, inscriptions are dated based on:


  • Their archaeological context. However, many texts are found in an unclear archaeological context, or first come to light in the antiquities market.

  • The epigrapher's creation of a typological structure of ancient Hebrew letters. This, however, is, by definition, not objective for it is often subject to considerations which are inevitably influenced by the researcher's cognitive world.


Our research seeks to enhance the second of these points ("traditional" typology) by using automated algorithms to the study of epigraphy. The efficiency of these algorithms have been proven in a great number of applications - from the formation of genetic proximity trees to analysis of financial market data. Algorithms have also been applied to graphological examination of contemporary manuscripts; yet, the ancient methods of writing, the evolution of the alphabet through the centuries, and the damage done to the artifacts in antiquity, do not lend themselves to use of off-the shelf products (in other words, existing scanning and OCR technologies are not readily applicable to ancient ostraca inscriptions).



Therefore, we carry out in-house development of the required technologies in the following stages:


  • Acquire better ostraca images.

  • Produce automated facsimile.

  • Examination of writing characteristics.

  • Clustering and creating letter typology.


Our aim is to create algorithmics with autonomic analysis skills that do not require a researcher's (subjective) involvement.

In addition to developing the computational abilities of handwriting analysis, we seek to improve data input by scanning the inscriptions using non-intrusive methods such as multispectral scanning techniques.