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Language Detection Library for Java


Language Support

71 Built-in Language Profiles

  1. af Afrikaans
  2. an Aragonese
  3. ar Arabic
  4. ast Asturian
  5. be Belarusian
  6. br Breton
  7. ca Catalan
  8. bg Bulgarian
  9. bn Bengali
  10. cs Czech
  11. cy Welsh
  12. da Danish
  13. de German
  14. el Greek
  15. en English
  16. es Spanish
  17. et Estonian
  18. eu Basque
  19. fa Persian
  20. fi Finnish
  21. fr French
  22. ga Irish
  23. gl Galician
  24. gu Gujarati
  25. he Hebrew
  26. hi Hindi
  27. hr Croatian
  28. ht Haitian
  29. hu Hungarian
  30. id Indonesian
  31. is Icelandic
  32. it Italian
  33. ja Japanese
  34. km Khmer
  35. kn Kannada
  36. ko Korean
  37. lt Lithuanian
  38. lv Latvian
  39. mk Macedonian
  40. ml Malayalam
  41. mr Marathi
  42. ms Malay
  43. mt Maltese
  44. ne Nepali
  45. nl Dutch
  46. no Norwegian
  47. oc Occitan
  48. pa Punjabi
  49. pl Polish
  50. pt Portuguese
  51. ro Romanian
  52. ru Russian
  53. sk Slovak
  54. sl Slovene
  55. so Somali
  56. sq Albanian
  57. sr Serbian
  58. sv Swedish
  59. sw Swahili
  60. ta Tamil
  61. te Telugu
  62. th Thai
  63. tl Tagalog
  64. tr Turkish
  65. uk Ukrainian
  66. ur Urdu
  67. vi Vietnamese
  68. wa Walloon
  69. yi Yiddish
  70. zh-cn Simplified Chinese
  71. zh-tw Traditional Chinese

User danielnaber has made available a profile for Esperanto on his website, see open tasks.

There are two kinds of profiles. The standard ones created from Wikipedia articles and similar. And the "short text" profiles created from Twitter tweets. Fewer language profiles exist for the short text, more would be available, see #57

Other Languages

You can create a language profile for your own language easily. See

How it Works

The software uses language profiles which were created based on common text for each language. N-grams were then extracted from that text, and that's what is stored in the profiles.

When trying to figure out in what language a certain text is written, the program goes through the same process: It creates the same kind of n-grams of the input text. Then it compares the relative frequency of them, and finds the language that matches best.


This software does not work as well when the input text to analyze is short, or unclean. For example tweets.

When a text is written in multiple languages, the default algorithm of this software is not appropriate. You can try to split the text (by sentence or paragraph) and detect the individual parts. Running the language guesser on the whole text will just tell you the language that is most dominant, in the best case.

This software cannot handle it well when the input text is in none of the expected (and supported) languages. For example if you only load the language profiles from English and German, but the text is written in French, the program may pick the more likely one, or say it doesn't know. (An improvement would be to clearly detect that it's unlikely one of the supported languages.)

If you are looking for a language detector / language guesser library in Java, this seems to be the best open source library you can get at this time. If it doesn't need to be Java, you may want to take a look at

How to Use

Language Detection for your Text

//load all languages:
List<LanguageProfile> languageProfiles = new LanguageProfileReader().readAllBuiltIn();

//build language detector:
LanguageDetector languageDetector = LanguageDetectorBuilder.create(NgramExtractors.standard())

//create a text object factory
TextObjectFactory textObjectFactory = CommonTextObjectFactories.forDetectingOnLargeText();

TextObject textObject = textObjectFactory.forText("my text");
Optional<LdLocale> lang = languageDetector.detect(textObject);

Creating Language Profiles for your Training Text


How You Can Help

If your language is not supported yet, then you can provide clean "training text", that is, common text written in your language. The text should be fairly long (a couple of pages at the very least). If you can provide that, please open a ticket.

If your language is supported already, but not identified clearly all the time, you can still provide such training text. We might then be able to improve detection for your language.

If you're a programmer, dig in the source and see what you can improve. Check the open tasks.

Memory Consumption

Loading all 71 language profiles uses 74MB ram to store the data in memory. For memory considerations see

History and Changes

This project is a fork of a fork, the original author is Nakatani Shuyo. For detail see

Where it's used

An adapted version of this is used by the server. is a proof-reading software for LibreOffice/OpenOffice, for the Desktop and for Firefox.


Apache 2 (business friendly)


Nakatani Shuyo, Fabian Kessler, Francois ROLAND, Robert Theis

For detail see

For Maven Users

The project is in Maven central this is the latest version: