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Show HN: Choosing font combinations with deep learning

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Tangentially, I've been trying to identify a font. I have a small example as a jpeg image. I tried https://www.myfonts.com/WhatTheFont/ without finding an exact match. Are there other good resources for doing this?

I suspect the tough part of whatthefont isn't so much the visual search but having a large database of labelled fonts.

There are other services that does this but personally I've always heard good things about whatthefont.

You might want to try http://www.identifont.com/.

You could try https://www.reddit.com/r/identifythisfont/, and while https://www.reddit.com/r/fontspotting/ does not seem to be directed toward identifying fonts in the sidebar, it is an active group and there are lots of such questions on the list.

Can you give us an Imgur link? A lot of people recognize more fonts than you might expect.

Interesting, but not all design is about harmony.

I'd be more interested in examples where ML could create combinations that shouldn't work together but work. Think like some of the more experimental stuff coming out of the Bloomberg Business Week design team

That raises an interesting debate about the boundary between experimental-but-brilliant and experimental-but-awful, and whether machine learning can meaningfully discern the difference and (ideally) provide the former. Because much of it is just taste and tastemaking; Bloomberg features, when posted here, often spark comment threads about how ugly or distracting the design is. Usually the majority of commenters side with the designers' apparent intent, but that's because Bloomberg is a relatively well-known/respected organization. Like modern art, experimental design's appeal is inextricably linked to the creator's credentials, e.g. "No one who gets paid that much would do such a crazy thing out of ignorance!".

So what would machine learning bring to the mix? I would prefer a heuristics-based analysis. That is, filter out the most popular combinations, and filter out combinations that are linked to "failed" designs (how you measure "failed" would be subjective of course). Then manually select, as a designer, from the uncommon but yet-unhated combinations left over.

One of my biggest complaints about things 'curated' via ML is that you are only introduced to things that are relatively similar. It's like trying to find new music by looking at the top 10 songs on the Billboard Chart.

Basically ML sucks at curating novelty.

I wonder, is it possible to generate fonts based on this algorithm, for character sets which are not included into the original font.

For example, I have English font, and want exactly same but with Hindy or Cyrillic characters. Any tips on how to implement it?

there's another project that does this: https://erikbern.com/2016/01/21/analyzing-50k-fonts-using-de...

the results are a bit blurry but as the author notes the next step is to use a better loss function. A GAN loss or GAN+L1 ala pix2pix should dramatically improve results.

This is pretty awesome. I've been working on my side project Mixfont (https://www.mixfont.com) which is a similar sort of font generator. Your pairings are a lot nicer than those on Mixfont though ... would love to learn more about how you did that.

it uses VGG16 to extract font features, then compares those features to figure out the best contrast between fonts.

feel free to use the vectors in the Github.

Although a title with deep learning in it makes me roll my eyes nowadays, I really should be more open to the ideas behind them. The font pairings at the end of the article look very nice!

I'm looking forward to "Choosing lunch with deep learning", trained on a curated selection of Instagram posts.

Perhaps this is what you want: https://www.ibmchefwatson.com/

Getting tired of living on the worldline where every time I think I'm making a joke, I turn out not to be.

Honestly most of the results are really poor. I would recommend using https://www.typewolf.com instead.

This does an alright job most of the time (7/10 options are decent). That's impressive.

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