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%.

本文介绍了一种使用混合模型的方法对 19 世纪 Fraktur 手写体进行 OCR，采用真实的数据训练混合模型的 OCR 引擎 Calamari 相较于 OCRopus、Tesseract 和 ABBYY 等其他引擎表现更佳，字符错误率平均降低超过 70％，平均字符错误率低于 1％。