The task
Language detection takes a string and returns its language, such as English, French, or Japanese. It runs before translation, search, and content filtering so each pipeline applies the right model.
A simple strong baseline
Languages have distinctive character n gram patterns. Counting short letter sequences captures the fingerprint of a language.
- Build a profile of common three and four character sequences per language.
- Compare a new text profile to each language profile.
- Pick the closest match.
This works even on short snippets and needs no grammar rules.
Practical challenges
- Short text like a single word gives weak evidence and raises errors.
- Code switching mixes two languages in one message, so a single label is wrong.
- Shared scripts make similar languages, such as Spanish and Portuguese, easy to confuse.
Confidence and fallbacks
Good detectors return a probability, not just a label. A low confidence score can trigger a fallback such as asking the user or defaulting to a safe language.
Key idea
Language detection compares a text's character n gram fingerprint to per language profiles, working well on short input but struggling with mixed languages and similar scripts.