Applications of Machine Learning in Document Digitisation
This work addresses the problem of costly and difficult data acquisition from paper documents for researchers and historians, offering an automated alternative to manual transcription.
This paper explores the use of machine learning for automating document digitisation, demonstrating its application in two scenarios. First, unsupervised layout classification was used on nurse journal scans to construct a treatment indicator and assess assignment compliance. Second, attention-based neural networks were applied for handwritten text recognition to transcribe age and dates from Danish death certificates.
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.