  Optical character recognition (OCR) is crucial for a deeper access to
historical collections. OCR needs to account for orthographic variations,
typefaces, or language evolution (i.e., new letters, word spellings), as the
main source of character, word, or word segmentation transcription errors. For
digital corpora of historical prints, the errors are further exacerbated due to
low scan quality and lack of language standardization.
  For the task of OCR post-hoc correction, we propose a neural approach based
on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to
correct OCR transcription errors. At character level we flexibly capture
errors, and decode the corrected output based on a novel attention mechanism.
Accounting for the input and output similarity, we propose a new loss function
that rewards the model's correcting behavior.
  Evaluation on a historical book corpus in German language shows that our
models are robust in capturing diverse OCR transcription errors and reduce the
word error rate of 32.3% by more than 89%.
