Comment Re:Did it really need AI? (Score 5, Informative) 47
Giant repositories of unreadable texts do not exist, as people tended to throw them away. A few exceptional archaeological discoveries have been spared; Dr. Seales, who perfected the scroll unrolling process, looked at a couple of them on the way to analysing the Herculaneum papyri. He has given exhaustingly long lectures on the topic in the past. The En-Gedi scroll used iron-based ink, which made the actual analysis trivial once the unrolling was complete.
The data being used in this case are X-ray spectrographs captured using a synchrotron. (A particle accelerator similar to the LHC.) This is the same technology used to perform crystallography on proteins, but tuned for a large object rather than millions or billions of copies of a single small molecule. This is powerful enough to reconstruct the scrolls on an atom-by-atom level, although it is not quite high enough resolution.
I can't emphasise enough that after 2000 years, there is no chemical difference between the burnt papyrus of the page and the burnt pine resin of the ink. All that exists in the physical object are patterns of how the stylus deformed the parchment during the writing process, and how the ink caused the fibrous structure to change as it dried. This is why many letters are damaged or left no trace at all.
Anyway. Image processing as a field is no longer a major topic that has large research grants behind it, and the experts who know the techniques are ageing. The field as a whole was basically killed dead in 2012, after the AlexNet model (a neural network) grossly outperformed the state of the art on the ImageNet challenge. I wouldn't go so far as saying all the researchers got sacked, but they definitely had to make a hard turn into new areas of research to keep their jobs.
This was an example of "the bitter lesson": There is no point in hand-crafting a large algorithm using expert knowledge when you can train an AI model to do the job 95% as well with 1% of the development time. Since expert knowledge of the data doesn't exist (and would take many researchers decades of work to divine, researchers who no longer even exist), there isn't a practical alternative.