  We investigate how to train a high quality optical character recognition
(OCR) model for difficult historical typefaces on degraded paper. Through
extensive grid searches, we obtain a neural network architecture and a set of
optimal data augmentation settings. We discuss the influence of factors such as
binarization, input line height, network width, network depth, and other
network training parameters such as dropout. Implementing these findings into a
practical model, we are able to obtain a 0.44% character error rate (CER) model
from only 10,000 lines of training data, outperforming currently available
pretrained models that were trained on more than 20 times the amount of data.
We show ablations for all components of our training pipeline, which relies on
the open source framework Calamari.
