  Optical Character Recognition (OCR) on contemporary and historical data is
still in the focus of many researchers. Especially historical prints require
book specific trained OCR models to achieve applicable results (Springmann and
L\"udeling, 2016, Reul et al., 2017a). To reduce the human effort for manually
annotating ground truth (GT) various techniques such as voting and pretraining
have shown to be very efficient (Reul et al., 2018a, Reul et al., 2018b).
Calamari is a new open source OCR line recognition software that both uses
state-of-the art Deep Neural Networks (DNNs) implemented in Tensorflow and
giving native support for techniques such as pretraining and voting. The
customizable network architectures constructed of Convolutional Neural Networks
(CNNS) and Long-ShortTerm-Memory (LSTM) layers are trained by the so-called
Connectionist Temporal Classification (CTC) algorithm of Graves et al. (2006).
Optional usage of a GPU drastically reduces the computation times for both
training and prediction. We use two different datasets to compare the
performance of Calamari to OCRopy, OCRopus3, and Tesseract 4. Calamari reaches
a Character Error Rate (CER) of 0.11% on the UW3 dataset written in modern
English and 0.18% on the DTA19 dataset written in German Fraktur, which
considerably outperforms the results of the existing softwares.
