  This paper proposes a combination of a convolutional and a LSTM network to
improve the accuracy of OCR on early printed books. While the standard model of
line based OCR uses a single LSTM layer, we utilize a CNN- and Pooling-Layer
combination in advance of an LSTM layer. Due to the higher amount of trainable
parameters the performance of the network relies on a high amount of training
examples to unleash its power. Hereby, the error is reduced by a factor of up
to 44%, yielding a CER of 1% and below. To further improve the results we use a
voting mechanism to achieve character error rates (CER) below $0.5%$. The
runtime of the deep model for training and prediction of a book behaves very
similar to a shallow network.
