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    <abstract>This paper explores the application of synthetic data in the post-OCR domain on multiple fronts by conducting experiments to assess the impact of data volume, augmentation, and synthetic data generation methods on model performance. Furthermore, we introduce a novel algorithm that leverages computer vision feature detection algorithms to calculate glyph similarity for constructing post-OCR synthetic data. Through experiments conducted across a variety of languages, including several low-resource ones, we demonstrate that models like ByT5 can significantly reduce Character Error Rates (CER) without the need for manually annotated data, and our proposed synthetic data generation method shows advantages over traditional methods, particularly in low-resource languages.</abstract>
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%0 Conference Proceedings
%T Advancing Post-OCR Correction: A Comparative Study of Synthetic Data
%A Guan, Shuhao
%A Greene, Derek
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F guan-greene-2024-advancing
%X This paper explores the application of synthetic data in the post-OCR domain on multiple fronts by conducting experiments to assess the impact of data volume, augmentation, and synthetic data generation methods on model performance. Furthermore, we introduce a novel algorithm that leverages computer vision feature detection algorithms to calculate glyph similarity for constructing post-OCR synthetic data. Through experiments conducted across a variety of languages, including several low-resource ones, we demonstrate that models like ByT5 can significantly reduce Character Error Rates (CER) without the need for manually annotated data, and our proposed synthetic data generation method shows advantages over traditional methods, particularly in low-resource languages.
%U https://aclanthology.org/2024.findings-acl.361
%P 6036-6047

Markdown (Informal)

[Advancing Post-OCR Correction: A Comparative Study of Synthetic Data](https://aclanthology.org/2024.findings-acl.361) (Guan & Greene, Findings 2024)

• Advancing Post-OCR Correction: A Comparative Study of Synthetic Data (Guan & Greene, Findings 2024)

ACL

• Shuhao Guan and Derek Greene. 2024. Advancing Post-OCR Correction: A Comparative Study of Synthetic Data. In Findings of the Association for Computational Linguistics ACL 2024, pages 6036–6047, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.