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http://www.let.rug.nl/vannoord/TextCat/
Python version:
http://thomas.mangin.com/data/source/ngram.py
It's something that is fun to implement and doesn't take more than a few hours at most.
If it's your own built service, it's critical how you explain the hows and whys of your forecast availability and scalability numbers for your chosen architecture, given who you are competing with.
[1]https://developers.google.com/translate/v2/using_rest#detect...
https://code.google.com/p/chromium-compact-language-detector...
Looks interesting. Why not have a input on the landing page where someone can try it out without even signing up? I think then people could give it a spin before they give away their email address. Otherwise, the user just has to trust your 99% figure, which it might be helpful to give some data around, even if it is a footnote (on a corpus of x, over x period of time, etc.) Also, I think it would be clearer if it sa…
how does this compare in accuracy to chromium's Compact Language Detector? https://code.google.com/p/chromium-compact-language-detector... https://github.com/mzsanford/cld
You should also consider full-non-ambiguous words before trying with trigrams. "marché" is only available in French, whereas "mar", "arc", ... are available in lots of languages. This should drastically improve your results.
Store only the top N common non-ambiguous words if the RAM consumption matters ;)
https://github.com/danieldk/dictomaton
Though, having implemented a language guesser myself, it's only an issue with very short texts (a few words). On longer texts models based on character n-grams achieve very high accuracies.