Edit: Reading some comments here - this doesn't seem to be about 'recognising' or 'predicting' existing characters, but using a dataset of characters to create a character by itself (which probably isn't an existing character).

This is quite 'clever'.

I don't understand the example shapes at the beginning. They're not correct strokes. How does that work?

The about page has some neat made up characters.

But after trying a few strokes, and doing so more carefully, it seems if you put in a clear radical, the character is well formed, kinda; if you put in a squiggle, all you get is a doodle... that makes sense.

Inputting 口 or 艹 for example, vs a random squiggle. Take care to make it reasonable accurate.

About characters, incase anyone doesn't know: A character is basically a 2x2 grid where 4 radicals get placed (there are (about) 201 radicals in modern Chinese, Japanese kanji too I guess?). Sometimes 'cells get merged' so the left column of 2 rows is merged to contain 1 radical, and the right contains 1 or 2 radicals. Or 'add a row' can happen at the top, for example adding a 艹 above the 2x2.