  Colonial archives are at the center of increased interest from a variety of
perspectives, as they contain traces of historically marginalized people.
Unfortunately, like most archives, they remain difficult to access due to
significant persisting barriers. We focus here on one of them: the biases to be
found in historical findings aids, such as indexes of person names, which
remain in use to this day. In colonial archives, indexes can perpetuate
silences by omitting to include mentions of historically marginalized persons.
In order to overcome such limitations and pluralize the scope of existing
finding aids, we propose using automated entity recognition. To this end, we
contribute a fit-for-purpose annotation typology and apply it on the colonial
archive of the Dutch East India Company (VOC). We release a corpus of nearly
70,000 annotations as a shared task, for which we provide baselines using
state-of-the-art neural network models. Our work intends to stimulate further
contributions in the direction of broadening access to (colonial) archives,
integrating automation as a possible means to this end.
