Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback

Christian Gold, Ronja Laarmann-Quante, Torsten Zesch


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.
Anthology ID:
2023.bea-1.28
Volume:
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
352–360
Language:
URL:
https://aclanthology.org/2023.bea-1.28
DOI:
10.18653/v1/2023.bea-1.28
Bibkey:
Cite (ACL):
Christian Gold, Ronja Laarmann-Quante, and Torsten Zesch. 2023. Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 352–360, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback (Gold et al., BEA 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.bea-1.28.pdf