Speakers
Description
In this paper, we present a new text recognition tool, Anagnostes, which
combines modern approaches in machine learning with optical character
recognition (OCR). It has been trained to read Greek literary and
documentary papyri from images, with a particular emphasis on
recognising not only neat and well-preserved fragments but also cursive
and damaged texts. Currently, the model achieves an average character
error rate (CER) of 10% across all types of Greek papyri. In our
presentation, we will provide a more detailed breakdown of CER across
various categories, including literary and documentary texts, as well as
clear, abraded, faded, and lacunose manuscripts. We will also discuss a
range of challenging features found in papyri, such as highly cursive
hands, quadrilinear scripts, heavily abbreviated texts, and the use of
special characters in documentary texts. These features are currently
covered only sporadically by our model. We will propose future training
strategies aimed at overcoming these challenges. Finally, we will
discuss how training can be adapted to target other languages
represented on papyri, such as Egyptian Hieratic, Egyptian Demotic,
Coptic, and Arabic.