@inproceedings{pavlopoulos-etal-2024-challenging,
title = "Challenging Error Correction in Recognised Byzantine {G}reek",
author = "Pavlopoulos, John and
Kougia, Vasiliki and
Garces Arias, Esteban and
Platanou, Paraskevi and
Shabalin, Stepan and
Liagkou, Konstantina and
Papadatos, Emmanouil and
Essler, Holger and
Camps, Jean-Baptiste and
Fischer, Franz",
editor = "Pavlopoulos, John and
Sommerschield, Thea and
Assael, Yannis and
Gordin, Shai and
Cho, Kyunghyun and
Passarotti, Marco and
Sprugnoli, Rachele and
Liu, Yudong and
Li, Bin and
Anderson, Adam",
booktitle = "Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)",
month = aug,
year = "2024",
address = "Hybrid in Bangkok, Thailand and online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ml4al-1.1",
pages = "1--12",
abstract = "Automatic correction of errors in Handwritten Text Recognition (HTR) output poses persistent challenges yet to be fully resolved. In this study, we introduce a shared task aimed at addressing this challenge, which attracted 271 submissions, yielding only a handful of promising approaches. This paper presents the datasets, the most effective methods, and an experimental analysis in error-correcting HTRed manuscripts and papyri in Byzantine Greek, the language that followed Classical and preceded Modern Greek. By using recognised and transcribed data from seven centuries, the two best-performing methods are compared, one based on a neural encoder-decoder architecture and the other based on engineered linguistic rules. We show that the recognition error rate can be reduced by both, up to 2.5 points at the level of characters and up to 15 at the level of words, while also elucidating their respective strengths and weaknesses.",
}
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%0 Conference Proceedings
%T Challenging Error Correction in Recognised Byzantine Greek
%A Pavlopoulos, John
%A Kougia, Vasiliki
%A Garces Arias, Esteban
%A Platanou, Paraskevi
%A Shabalin, Stepan
%A Liagkou, Konstantina
%A Papadatos, Emmanouil
%A Essler, Holger
%A Camps, Jean-Baptiste
%A Fischer, Franz
%Y Pavlopoulos, John
%Y Sommerschield, Thea
%Y Assael, Yannis
%Y Gordin, Shai
%Y Cho, Kyunghyun
%Y Passarotti, Marco
%Y Sprugnoli, Rachele
%Y Liu, Yudong
%Y Li, Bin
%Y Anderson, Adam
%S Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Hybrid in Bangkok, Thailand and online
%F pavlopoulos-etal-2024-challenging
%X Automatic correction of errors in Handwritten Text Recognition (HTR) output poses persistent challenges yet to be fully resolved. In this study, we introduce a shared task aimed at addressing this challenge, which attracted 271 submissions, yielding only a handful of promising approaches. This paper presents the datasets, the most effective methods, and an experimental analysis in error-correcting HTRed manuscripts and papyri in Byzantine Greek, the language that followed Classical and preceded Modern Greek. By using recognised and transcribed data from seven centuries, the two best-performing methods are compared, one based on a neural encoder-decoder architecture and the other based on engineered linguistic rules. We show that the recognition error rate can be reduced by both, up to 2.5 points at the level of characters and up to 15 at the level of words, while also elucidating their respective strengths and weaknesses.
%U https://aclanthology.org/2024.ml4al-1.1
%P 1-12
Markdown (Informal)
[Challenging Error Correction in Recognised Byzantine Greek](https://aclanthology.org/2024.ml4al-1.1) (Pavlopoulos et al., ML4AL-WS 2024)
ACL
- John Pavlopoulos, Vasiliki Kougia, Esteban Garces Arias, Paraskevi Platanou, Stepan Shabalin, Konstantina Liagkou, Emmanouil Papadatos, Holger Essler, Jean-Baptiste Camps, and Franz Fischer. 2024. Challenging Error Correction in Recognised Byzantine Greek. In Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024), pages 1–12, Hybrid in Bangkok, Thailand and online. Association for Computational Linguistics.