> The publisher's IT dept really only cares that the ads still show up in enough places, not about giving us a useful tool to work with.
That's really unfortunate, because your technical content is among the, if not the very best in the business. But clearly there hasn't been much investment into the presentation side over the last years.
There's this German hardware review site [1] that has an almost embarrassingly good system for graphs like this, I'm sure you are aware of it. Would be really amazing if we could somehow get that level of presentation for your content!
To back up the other comment, recent studies find that students from worse secondary schools with worse grades end up outperforming students from more selective schools with better grades once in medical school.
So in order to get the best doctors you can't just be all "meritocratic" by looking only at the results, i.e. grades, but have to take other factors into account.
This is how traditional desktop operating systems work... some things require admin rights but most stuff is indeed fully accessible to any application.
To back up what the other comments are saying, recent studies find that students from worse secondary schools with worse grades end up outperforming students from more selective schools with better grades once in medical school.
So, if you want the cream to rise... take it from the bottom.
Note that this paper is from 2010 and thus, while quite influential for its time far from the current state-of-the-art. The stroke width transform method that it introduced is simply not as good as current deep learning-based methods.
If you want to get a (slightly out of date but what can you do, the field is moving very fast) overview see this survey from 2016:
If you're trying to handle text "in the wild" and not scanned documents, the keyword is "scene text". Most papers are focused on either detection/localization, i.e. finding the location of text, or recognition, i.e. recognizing the actual content given a cropped text image.
Here are some current state-of-the-art papers + code where available about detection:
Current state-of-the-art approaches in this field are significantly better than existing commercial solutions at recognizing text in the wild (i.e. scene text).
As an example, see the ICDAR 2015 results [1], where the Google Vision API is at 59.60% (Hmean) while the best ones are over 80%. Note that this test is about localization, i.e. finding the text location without recognizing the actual content, though on a more challenging dataset.
As for recognition, see the table on page 6 of this paper [2]. The "IIIT5K None" column should be pretty close to what was done in the OP, using the same dataset, with recognition accuracies of around 80% while the Google Vision API is at 322/500=64.4%. Note here that since this paper is only about recognition, there is no localization step before which would otherwise act as a filter and decrease the accuracy a bit by failing to localize some text that the recognition step would be able to recognize.
That's really unfortunate, because your technical content is among the, if not the very best in the business. But clearly there hasn't been much investment into the presentation side over the last years.
There's this German hardware review site [1] that has an almost embarrassingly good system for graphs like this, I'm sure you are aware of it. Would be really amazing if we could somehow get that level of presentation for your content!
[1] https://www.computerbase.de/2020-09/samsung-980-pro-ssd-test...