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Very interesting! I don't think that I ever had a sample from a non-standard keyboard layout in the dataset.

While it might sound like this should make your samples very different from others, it could actually act the other way and confuse the network. Hopefully, this will be improved in the next version of it, which I'll start training right after this data collection sprint.

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I popped a few more in until I hit the maximum 91 stored (either that or it broke). Thanks for fixing the way the space character is displayed.

You could use https://developer.mozilla.org/en-US/docs/Web/API/KeyboardEve... to take advantage of keyboard layout information to more easily classify a user. E.g. if a German user is typing in English their key will register as "KeyY" but their key will register as "z". Even if the neural net just picks up on "non qwerty = weird" I think it would significantly help in it's ability to classify these corner case users.

I look forward to seeing how future versions fare!

What about those corner case users that use a non-qwerty layout but the layout is in hardware (or keyboard firmware) rather than software?

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