# Toward a Period-Specific Optimized Neural Network for OCR Error Correction of Historical Hebrew Texts

arXiv: https://arxiv.org/abs/2307.16213 · HTML: https://mlsanity.com/paper/2307.16213/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 30 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Comparative Study of CNN and RNN for Natural Language Processing](https://mlsanity.com/paper/1702.01923/) (2017)
- [What's Wrong with Hebrew NLP? And How to Make it Right](https://mlsanity.com/paper/1908.05453/) (2019)
- [Neural Networks for Text Correction and Completion in Keyboard Decoding](https://mlsanity.com/paper/1709.06429/) (2017)
- [OCR Error Correction Using Character Correction and Feature-Based Word Classification](https://mlsanity.com/paper/1604.06225/) (2016)
- [Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit](https://mlsanity.com/paper/1809.02147/) (2018)
- [From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction](https://mlsanity.com/paper/1910.05535/) (2019)
- [Toward the Optimized Crowdsourcing Strategy for OCR Post-Correction](https://mlsanity.com/paper/2106.06831/) (2021)
- [Neural Network Architecture Optimization through Submodularity and Supermodularity](https://mlsanity.com/paper/1609.00074/) (2016)
- [Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction](https://mlsanity.com/paper/1906.10907/) (2019)

## Built on by (most cited first, 2 of 2)

- [Ancient Script Image Recognition and Processing: A Review](https://mlsanity.com/paper/2506.19208/) (2025)
- [Geospatial Data Fusion: Combining Lidar, SAR, and Optical Imagery with AI for Enhanced Urban Mapping](https://mlsanity.com/paper/2412.18994/) (2024)

## Abstract (arXiv)

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.