@inproceedings{gold-etal-2023-recognizing,
title = "Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback",
author = "Gold, Christian and
Laarmann-Quante, Ronja and
Zesch, Torsten",
editor = {Kochmar, Ekaterina and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Madnani, Nitin and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng and
Zesch, Torsten},
booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bea-1.28",
doi = "10.18653/v1/2023.bea-1.28",
pages = "352--360",
abstract = "This paper addresses the problem of providing automatic feedback on orthographic errors in handwritten text. Despite the availability of automatic error detection systems, the practical problem of digitizing the handwriting remains. Current handwriting recognition (HWR) systems produce highly accurate transcriptions but normalize away the very errors that are essential for providing useful feedback, e.g. orthographic errors. Our contribution is twofold:First, we create a comprehensive dataset of handwritten text with transcripts retaining orthographic errors by transcribing 1,350 pages from the German learner dataset FD-LEX. Second, we train a simple HWR system on our dataset, allowing it to transcribe words with orthographic errors. Thereby, we evaluate the effect of different dictionaries on recognition output, highlighting the importance of addressing spelling errors in these dictionaries.",
}
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%0 Conference Proceedings
%T Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback
%A Gold, Christian
%A Laarmann-Quante, Ronja
%A Zesch, Torsten
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gold-etal-2023-recognizing
%X This paper addresses the problem of providing automatic feedback on orthographic errors in handwritten text. Despite the availability of automatic error detection systems, the practical problem of digitizing the handwriting remains. Current handwriting recognition (HWR) systems produce highly accurate transcriptions but normalize away the very errors that are essential for providing useful feedback, e.g. orthographic errors. Our contribution is twofold:First, we create a comprehensive dataset of handwritten text with transcripts retaining orthographic errors by transcribing 1,350 pages from the German learner dataset FD-LEX. Second, we train a simple HWR system on our dataset, allowing it to transcribe words with orthographic errors. Thereby, we evaluate the effect of different dictionaries on recognition output, highlighting the importance of addressing spelling errors in these dictionaries.
%R 10.18653/v1/2023.bea-1.28
%U https://aclanthology.org/2023.bea-1.28
%U https://doi.org/10.18653/v1/2023.bea-1.28
%P 352-360
Markdown (Informal)
[Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback](https://aclanthology.org/2023.bea-1.28) (Gold et al., BEA 2023)
ACL