  Over the past few decades, large archives of paper-based historical
documents, such as books and newspapers, have been digitized using the Optical
Character Recognition (OCR) technology. Unfortunately, this broadly used
technology is error-prone, especially when an OCRed document was written
hundreds of years ago. Neural networks have shown great success in solving
various text processing tasks, including OCR post-correction. The main
disadvantage of using neural networks for historical corpora is the lack of
sufficiently large training datasets they require to learn from, especially for
morphologically-rich languages like Hebrew. Moreover, it is not clear what are
the optimal structure and values of hyperparameters (predefined parameters) of
neural networks for OCR error correction in Hebrew due to its unique features.
Furthermore, languages change across genres and periods. These changes may
affect the accuracy of OCR post-correction neural network models. To overcome
these challenges, we developed a new multi-phase method for generating
artificial training datasets with OCR errors and hyperparameters optimization
for building an effective neural network for OCR post-correction in Hebrew.
