  This paper discusses how to successfully digitize large-scale historical
micro-data by augmenting optical character recognition (OCR) engines with pre-
and post-processing methods. Although OCR software has improved dramatically in
recent years due to improvements in machine learning, off-the-shelf OCR
applications still present high error rates which limit their applications for
accurate extraction of structured information. Complementing OCR with
additional methods can however dramatically increase its success rate, making
it a powerful and cost-efficient tool for economic historians. This paper
showcases these methods and explains why they are useful. We apply them against
two large balance sheet datasets and introduce quipucamayoc, a Python package
containing these methods in a unified framework.
