I am learning about the problem of whole-book recognition, which is tangential to optical character recognition. Some of the strategies used to identify printed characters rely on first unsupervised clustering of letters by their visual characteristics, then solving the resulting cryptogram to create a training set, then performing a supervised classification for the rest of the data.

As far as I can tell, this kind of bootstrapping is related to but distinct from semi-supervised learning, in which a subset of training labels are available from the get-go.

Does such a two-step learning process, unsupervised and then supervised, have a specific name?