Interactive Font Tree in FontClustr

I’m pleased to announce that the latest version of FontClustr, which is freely available on GitHub, now supports an interactive HTML tree for browsing its output.

This is a step toward an interactive font chooser that sorts by appearance.

When I go looking for a good typeface, I usually have a few styles in mind that I want to try out. Once I pass over some fonts that I definitely don’t want to use, it’s handy to be able to collapse the list. This is now possible, by clicking on one of the colored vertical bars to the left of the font previews.

In this case, I am clicking on the bright red bar to hide all the entries attached to it.

Currently, this action is not very aesthetic; the tree is changed instantly — without animation. Additionally, the excess space is not taken up in a logical way, so if you collapse a big section then it might be ambiguous as to whether the other fonts slid up or down to fill the void.

Still, its one less thing between me and the perfect typeface for whatever I’m doing.

Not counting the padding the font previews here are 50px in size. Collapsing them makes them fit into 20% of the original amount of space.

FontClustr Receives Honorable Mention

My FontClustr project has received honorable mention in the 2011 Catalyst Award competition! As an outsider to the world of graphic design, I’m touched that they found my work so inspiring.

From the press release:

The judges were impressed by the approach taken by this self-confessed non-typographer to a practical problem: how to automate grouping of different typefaces based on design similarity, so that users can see them “in the context of the visual landscape they collectively form”. … [The] FontClustr tool is an admirable example of an outsider’s analytical approach to the problem of typeface categorisation or grouping.

FontClustr: It’s yours, free.

The software I wrote in January called FontClustr is now available under the GPL.

If you use my methodology to improve font selection in your own program, I would appreciate the hell out of it if you credited me in some way.

For the impatient, you’ll need the following:

Run it by making executable and executing it. If all goes well, you’ll be in for about 4 hours and 400MB to make all the output. At least, that’s what it took on my machine with about 1000 installed fonts.

Hit the jump for all the boring stuff.
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Working Demo of FontClustr Output (for Ubuntu Fonts)

I’ve expanded FontClustr to be an interactive Javascript application.

Bear in mind, this HTML was generated by running the clustering algorithm on my specific computer — your installed fonts will be different if you check this out on a non-Ubuntu machine. In fact, if you don’t have ALL of Ubuntu’s font packages installed, you may see a lot of fonts that look the same; your system is switching to a default font.

On the other hand, if you have access to an Ubuntu machine, you’ll be able to experiment with the sample text, its size, and colors.

I will try to get access to a Mac so that I can do some final checks on the code. The next hurdle will be trying to get Python modules installed in a Windows machine.

FontClustr – Automated Hierarchichal Clustering of Fonts Based On Their Appearance

Something has always bothered me about fonts: I have to pick one alphabetically.

I have over 1200 fonts on my computer. Why am I forced to pick the perfect one by going through an alphabetical list? Not even the major font families (serif, sans-serif, condensed, cursive, fantasy, etc) are grouped together.

No longer. I’ve written a program in python that can hierarchically cluster fonts based on their appearance. For your enjoyment, I’ve picked 35 of the best clusters (this is actually more than 80% of the total output) to illustrate how powerful this technique is. Hit the jump for those.

If you are a software company that makes a product with a font selection dialog (like Word, Photoshop, Gimp, Inkscape, Illustrator, Powerpoint, etc), PLEASE START DOING THIS. I WILL BE HAPPY TO HELP YOU.

Let me stress this again, the screenshots you’re seeing here were from an automated font comparison and clustering program.

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