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Methods for linking individuals across historical data sets, typically incombination with AI based transcription models, are developing rapidly.Probably the single most important identifier for linking is personal names.However, personal names are prone to enumeration and transcription errors andalthough modern linking methods are designed to handle such challenges, thesesources of errors are critical and should be minimized. For this purpose,improved transcription methods and large-scale databases are crucialcomponents. This paper describes and provides documentation for HANA, a newlyconstructed large-scale database which consists of more than 3.3 million names.The database contain more than 105 thousand unique names with a total of morethan 1.1 million images of personal names, which proves useful for transferlearning to other settings. We provide three examples hereof, obtainingsignificantly improved transcription accuracy on both Danish and US censusdata. In addition, we present benchmark results for deep learning modelsautomatically transcribing the personal names from the scanned documents.Through making more challenging large-scale databases publicly available wehope to foster more sophisticated, accurate, and robust models for handwrittentext recognition.

HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition