Is it only me? I think I lost the ability to read cursive handwriting. I tried reading a few pages, and it is really hard for me to follow what is written.
But really, cursive is optimized for reducing the possibility of failure (inkblots) on primitive quill pens, not for readability. Even the best most precise cursive tends to be more difficult to read than modestly competent print.
That's purely a practice effect, like the fake finding that lowercase text is easier to read than all-caps text. (People you pull off the street do indeed read lowercase text faster. After a minor amount of practice, this advantage disappears.)
There are a lot of cases here where a series of letters appears to have been reduced to just a wave pattern. But the biggest problems I have at first glance are
- You can't zoom in far enough
- Some letters are written in an unexpected (consistent, but not modern) way. Look at how e is always written like you'd expect a cursive o or a σ to look. This difficulty will be easily overcome if you spend any time reading the text, but as of now I have a lot more trouble identifying what letters have been written than identifying what word a sequence of known letters is supposed to represent.
On the other hand, a lot of it looks perfectly fine. "This occasioned much discourse of the cause of Fountaines, and Dr Robinson was of opinion that (stormes?) occasiond by a subterraneall heat, either of fire or" (eighth bullet point at the beginning of 1686).
Have you used Zooniverse in the past? If so, what was the outcome/experience like?
My research group actually has a project in beta at the moment on there: https://www.zooniverse.org/projects/rossmcwhirter/astro-ecol...
We're asking people to tag animals in infrared images. There isn't much data on there yet while we figure out the simplest way to get people involved (feedback driven). The site engine is pretty good, it's very easy to set up your own data campaign. It's quite challenging to frame your problem in a way that people with no domain knowledge can label your data accurately.
The only disadvantage (from a research perspective) is that you need exposure and traction to really succeed, because you need a huge number of classifications (multiple times per image) to get good results. If you look, there are a lot of projects with quite a lot of classifications but 0% completion. Certain categories are just popular - e.g. exoplanet detection and astronomy always gets a lot of interest.