Applications of machine learning in tabular document digitisation

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 imply costly and difficult, especially when the data medium is paper and books. Human operators and manual transcription has been the traditional approach for collecting historical data. We instead advocate the use of modern machine learning techniques to automate the digitization and transcription process. We propose a customizable end-to-end transcription pipeline to perform layout classification, table segmentation, and transcribe handwritten text that is suitable for tabular data, as is common in, e.g., census lists and birth and death records. We showcase our pipeline through two applications: The first demonstrates that unsupervised layout classification applied to raw scans of nurse journals can be used to obtain valuable insights into an extended nurse home visiting program. The second application uses attention-based neural networks for handwritten text recognition to transcribe age and birth and death dates and includes a comparison to automated transcription using Transkribus in the regime of tabular data. We describe each step in our pipeline and provide implementation insights.