Earlier quoted context omitted.
If your eyes can "normalize" a unusual symbols to a common one to make an English word then so can a lookup table. I feel like this isn't a case where you'd reach first for a neural net.
If you can identify text written with mixed glyphs just ban it outright. Normal users don't use text like this, the pure binary presence of such "homomorphic" text at all is probably a better signal for spam than whatever your neural net when running it after normalization.
I think that depends on the users. People copying and pasting bits of text that was in English or another common language— think documentation, code, news articles, tweets, etc.— with a different character set could be problematic.
Also, 𝒮ℴ𝓂ℯ 𝒜𝓅𝓅𝓈 marketed as "𝔽𝕠𝕟𝕥𝕤 𝕗𝕠𝕣 𝕤𝕠𝕔𝕒𝕝 𝕞𝕖𝕕𝕚𝕒" would be ℭ𝔞𝔲𝔤𝔥𝔱 𝔲𝔭 𝔦𝔫 𝔱𝔥𝔦𝔰. (math symbols) A user base with young people getting bounced or shadow banned for trying to express themselves or distinguish themselves from their peers would be like ಠ_ಠ (Kannada letter ttha)
I think targeting the language they're using is a better bet.
¯\_(ツ)_/¯ (Hirigana letter tsu)