It seems the project has made a trade-off about language support.

One approach would be to say language doesn't matter, just train on converting any character from any language alphabet from image to text. The problem is that higher accuracy can be achieved by isolating characters from each language from each other. I imagine that particularly for Latin alphabet languages, accuracy must improve dramatically by splitting out any kanji or hanzi.

Even directly for Latin alphabet languages you would typically want to know the language, both to reduce the charset (i.e. the distinctions between smudgy ã or â or ā or ä or á often are trivial if you know the language but may be quite hard to tell otherwise) and to use a proper language model for disambiguating individual character guesses.