  Witnesses of medieval literary texts, preserved in manuscript, are layered
objects , being almost exclusively copies of copies. This results in multiple
and hard to distinguish linguistic strata -- the author's scripta interacting
with the scriptae of the various scribes -- in a context where literary written
language is already a dialectal hybrid. Moreover, no single linguistic
phenomenon allows to distinguish between different scriptae, and only the
combination of multiple characteristics is likely to be significant [9] -- but
which ones? The most common approach is to search for these features in a set
of previously selected texts, that are supposed to be representative of a given
scripta. This can induce a circularity, in which texts are used to select
features that in turn characterise them as belonging to a linguistic area. To
counter this issue, this paper offers an unsupervised and corpus-based
approach, in which clustering methods are applied to an Old French corpus to
identify main divisions and groups. Ultimately, scriptometric profiles are
built for each of them.
