  We present our demonstration of two machine translation applications to
historical documents. The first task consists in generating a new version of a
historical document, written in the modern version of its original language.
The second application is limited to a document's orthography. It adapts the
document's spelling to modern standards in order to achieve an orthography
consistency and accounting for the lack of spelling conventions. We followed an
interactive, adaptive framework that allows the user to introduce corrections
to the system's hypothesis. The system reacts to these corrections by
generating a new hypothesis that takes them into account. Once the user is
satisfied with the system's hypothesis and validates it, the system adapts its
model following an online learning strategy. This system is implemented
following a client-server architecture. We developed a website which
communicates with the neural models. All code is open-source and publicly
available. The demonstration is hosted at http://demosmt.prhlt.upv.es/mthd/.
