  Data acquisition forms the primary step in all empirical research. The
availability of data directly impacts the quality and extent of conclusions and
insights. In particular, larger and more detailed datasets provide convincing
answers even to complex research questions. The main problem is that 'large and
detailed' usually implies 'costly and difficult', especially when the data
medium is paper and books. Human operators and manual transcription have been
the traditional approach for collecting historical data. We instead advocate
the use of modern machine learning techniques to automate the digitisation
process. We give an overview of the potential for applying machine digitisation
for data collection through two illustrative applications. The first
demonstrates that unsupervised layout classification applied to raw scans of
nurse journals can be used to construct a treatment indicator. Moreover, it
allows an assessment of assignment compliance. The second application uses
attention-based neural networks for handwritten text recognition in order to
transcribe age and birth and death dates from a large collection of Danish
death certificates. We describe each step in the digitisation pipeline and
provide implementation insights.

Computer Vision and Pattern Recognition;Econometrics;Machine Learning