OCR Correction with ByT5

We have developed a Dutch OCR correction model using the ByT5 architecture, which is capable of identifying and rectifying OCR mistakes. Optical Character Recognition (OCR) technology is widely used to convert scanned documents into digitized text, but it often produces errors. To automate the manual post-correction phase, we trained the ByT5 model on a large Dutch dataset and simulated OCR mistakes using the nlpaug library. ByT5, a token-free model that operates on raw bytes of text, proves to be more resistant to noisy data compared to token-based models. Our implementation, which includes dataset loading, model training, and inference, demonstrates the effectiveness of the ByT5 model in OCR correction tasks. The results highlight its advantages over token-based models for small to medium-sized sentences with high noise levels. This OCR correction model provides a powerful solution for automating the post-processing phase and improving the accuracy of OCR outputs.

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