
Restoring ancient text using deep learning: a case study on Greek epigraphy - breck
https://arxiv.org/abs/1910.06262
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romaaeterna
There is something badly wrong with this paper. Their example shows them
restoring μηδέν ἄγαν from μηδέν ἄ??ν. The diacritics are human provided, and
_very_ much limit the search space. The inscription to start with would
actually be μηδενα??ν (that is, if the number of characters between α and ν is
correct, which is also a human provided guess.

All of their other examples seem to start with diacritics as well. What a
mistake!

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knolax
Reminds me of the algorithm from a Canticle for Leibowitz one of the monks was
working on. I do have to wonder if things like this do more harm than good. A
guess done by deep learning is still just a guess, the difference being the
additional danger of it looking more authoritive and certain than it is.

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shakna
It seems like this particular alogrithm has two main components that are
highly important, and human-oriented:

a) The search space is limited with human-supplied options.

b) The output isn't deterministic, is offers a series of best guesses.

I would say this is closer to a NN-powered spellchecker than an authoritative
source on the restoration of the work. It still requires a decent amount of
interaction from an actual expert for the system to work.

