Nice followup! I hope @Silvia Hao will see this. And I am also looking forward to the animated Rorschach post. :-)

Thank you for your feedback, Silvia!

[...] I'm always a big fan of traditional statistical analysis!

That comment provided additional motivation to "wrap up" a first version of the post "Handwritten Arabic characters classifiers comparison".

In that post I show how using dimension reduction and nearest neighbors we can get better classification results than using "standard" convolutional networks. Of course, we also get additional insight.

One central interest on characters and fonts (not just CJK) is the topological relationship between strokes. It's somehow related but beyond properties in image space.

1. In the linked post I had to crop-&-resize the images in order to get better results. That is, in some sense, related to having hard time to "discover" the topology in Arabic characters.

2. Another approach is to actually make some cheap transformations that expose the topology. That is, of course, not that trivial.

3. People writing Chinese characters -- after years of schooling -- follow certain order of the strokes. I think that is much more fruitful type of data utilization. (And as far as I know it is used in the Chinese character completions in different OS'es.)

Just a teaser here -- I plan to use the code (and explanations) in this post as a basis for submitting animated Rorschach test images for the "Computational Art Contest 2022".

Other versions:

1. WordPress blog post (HTML)

   • Made with the package M2MD.

2. GitHub post (Markdown)

   • Made with pandoc.

Just curious how close your reduced set of auto radicals compared to those of the Cang Jie Input radicals?

Thank you for the recognition, Moderation Team!