  This paper introduces a novel approach to post-Optical Character Recognition
Correction (POC) for handwritten Cyrillic text, addressing a significant gap in
current research methodologies. This gap is due to the lack of large text
corporas that provide OCR errors for further training of language-based POC
models, which are demanding in terms of corpora size. Our study primarily
focuses on the development and application of a synthetic handwriting
generation engine based on B\'ezier curves. Such an engine generates highly
realistic handwritten text in any amounts, which we utilize to create a
substantial dataset by transforming Russian text corpora sourced from the
internet. We apply a Handwritten Text Recognition (HTR) model to this dataset
to identify OCR errors, forming the basis for our POC model training. The
correction model is trained on a 90-symbol input context, utilizing a
pre-trained T5 architecture with a seq2seq correction task. We evaluate our
approach on HWR200 and School_notebooks_RU datasets as they provide significant
challenges in the HTR domain. Furthermore, POC can be used to highlight errors
for teachers, evaluating student performance. This can be done simply by
comparing sentences before and after correction, displaying differences in
text. Our primary contribution lies in the innovative use of B\'ezier curves
for Cyrillic text generation and subsequent error correction using a
specialized POC model. We validate our approach by presenting Word Accuracy
Rate (WAR) and Character Accuracy Rate (CAR) results, both with and without
post-OCR correction, using real open corporas of handwritten Cyrillic text.
These results, coupled with our methodology, are designed to be reproducible,
paving the way for further advancements in the field of OCR and handwritten
text analysis. Paper contributions can be found in
https://github.com/dbrainio/CyrillicHandwritingPOC
