  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.
