Handwritten Paleographic Greek Text Recognition: A Century-Based Approach

Paraskevi Platanou, John Pavlopoulos, Georgios Papaioannou


Abstract
Today classicists are provided with a great number of digital tools which, in turn, offer possibilities for further study and new research goals. In this paper we explore the idea that old Greek handwriting can be machine-readable and consequently, researchers can study the target material fast and efficiently. Previous studies have shown that Handwritten Text Recognition (HTR) models are capable of attaining high accuracy rates. However, achieving high accuracy HTR results for Greek manuscripts is still considered to be a major challenge. The overall aim of this paper is to assess HTR for old Greek manuscripts. To address this statement, we study and use digitized images of the Oxford University Bodleian Library Greek manuscripts. By manually transcribing 77 images, we created and present here a new dataset for Handwritten Paleographic Greek Text Recognition. The dataset instances were organized by establishing as a leading factor the century to which the manuscript and hence the image belongs. Experimenting then with an HTR model we show that the error rate depends on the century of the image.
Anthology ID:
2022.lrec-1.708
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6585–6589
Language:
URL:
https://aclanthology.org/2022.lrec-1.708
DOI:
Bibkey:
Cite (ACL):
Paraskevi Platanou, John Pavlopoulos, and Georgios Papaioannou. 2022. Handwritten Paleographic Greek Text Recognition: A Century-Based Approach. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6585–6589, Marseille, France. European Language Resources Association.
Cite (Informal):
Handwritten Paleographic Greek Text Recognition: A Century-Based Approach (Platanou et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.708.pdf
Code
 vivianpl/hpgtr