# State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines

arXiv: https://arxiv.org/abs/1810.03436 · HTML: https://mlsanity.com/paper/1810.03436/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Neural OCR Post-Hoc Correction of Historical Corpora](https://mlsanity.com/paper/2102.00583/) (2021)
- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning](https://mlsanity.com/paper/2106.07881/) (2021)
- [Making Old Kurdish Publications Processable by Augmenting Available Optical Character Recognition Engines](https://mlsanity.com/paper/2404.06101/) (2024)

## Abstract (arXiv)

In this paper we evaluate Optical Character Recognition (OCR) of 19th century Fraktur scripts without book-specific training using mixed models, i.e. models trained to recognize a variety of fonts and typesets from previously unseen sources. We describe the training process leading to strong mixed OCR models and compare them to freely available models of the popular open source engines OCRopus and Tesseract as well as the commercial state of the art system ABBYY. For evaluation, we use a varied collection of unseen data from books, journals, and a dictionary from the 19th century. The experiments show that training mixed models with real data is superior to training with synthetic data and that the novel OCR engine Calamari outperforms the other engines considerably, on average reducing ABBYYs character error rate (CER) by over 70%, resulting in an average CER below 1%.