  Methods for linking individuals across historical data sets, typically in
combination 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 and
although modern linking methods are designed to handle such challenges these
sources of errors are critical and should be minimized. For this purpose,
improved transcription methods and large-scale databases are crucial
components. This paper describes and provides documentation for HANA, a newly
constructed large-scale database which consists of more than 1.1 million images
of handwritten word-groups. The database is a collection of personal names,
containing more than 105 thousand unique names with a total of more than 3.3
million examples. In addition, we present benchmark results for deep learning
models that automatically can transcribe the personal names from the scanned
documents. Focusing mainly on personal names, due to its vital role in linking,
we hope to foster more sophisticated, accurate, and robust models for
handwritten text recognition through making more challenging large-scale
databases publicly available. This paper describes the data source, the
collection process, and the image-processing procedures and methods that are
involved in extracting the handwritten personal names and handwritten text in
general from the forms.
