State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines
Abstract
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%.
Keywords
Cite
@article{arxiv.1810.03436,
title = {State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines},
author = {Christian Reul and Uwe Springmann and Christoph Wick and Frank Puppe},
journal= {arXiv preprint arXiv:1810.03436},
year = {2018}
}
Comments
Submitted to DHd 2019 (https://dhd2019.org/) which demands a... creative... submission format. Consequently, some captions might look weird and some links aren't clickable. Extended version with more technical details and some fixes to follow