Handwriting recognition is an important but very complicated domain of Computer Sciences. Computers nowadays do a quite fair job even if there is room for improvement in the future.
I am wondering if the result is language-dependant: 1) Are there languages that are more easily analyzed by handwriting recognition programs? Why? 2) Quality of recognition depends probably on individuals' handwriting. If there are difference of quality of recognitions between language, is this difference significantly larger or smaller than the individual variation?
Concerning the first question, my intuition is that best performances are obtained with languages with a lot of writers like English (more resources to analyze, more motivation, more money,...) or with a particular alphabet/language structure (Korean and its simple alphabet ㄱㄴㄷㄹㅁㅂㅅㅏㅓㅜ seems to be a good candidate).
Edit: I am not sure if the relevant context is language or alphabet. As Tom pointed out in comments, different languages with the same alphabet may behave differently in language recognization. For example, I think that a basic strategy when some letters of a word are identified is comparison with a dictionary. German, with its possibility of creating new words by concatenation, makes this strategy (slightly?) less useful than English. Language structure like genders may also help.
My motivation is to understand some ideas of OCR, so at the end of the day, I'll accept a good answer, even if it is focused about alphabet rather than language.