AI Language Detector

What this language detector does

Type or paste a piece of text, press Detect Language, and the tool names the language it is written in and shows how confident it is. The work is done by MediaPipe's language-detection model running inside your browser through WebAssembly, so the text you paste is not sent to a server for analysis. The first time you use it, the page downloads the model and its runtime; after that, detection is local.

It is a language identifier, not a translator. It tells you what language the text appears to be, gives the ISO-style language code, and reports a probability. It does not convert the text, correct it, or explain its meaning.

How the detection works

The page loads a small text-classification model (a TensorFlow Lite file) together with MediaPipe's text tasks runtime. When you submit text, the model scores it against the languages it was trained on and returns a ranked list. The tool displays the top result: the language name, its code, and the probability as a percentage.

Everything after the model download happens on your device. The textarea, the character counter, the Detect button and the result card are all local JavaScript. That is why the tool can work without an account and without sending your paragraph anywhere.

The first load and the “Load AI Model” button

Because the model is a separate file, the tool does not fetch it until it is needed. You can press Load AI Model to start the download in advance, or simply press Detect and let it load automatically. While it loads, the page shows the current step (“Preparing AI engine”, “Downloading modules”, “Loading language model”). When it finishes, the button changes to Model Ready.

If the download fails, the button turns red and the status line shows the error. That is usually a network or content-blocker problem rather than a problem with your text. Reload the page and try again, or allow requests to the CDN and model host used by the tool.

Which languages are covered

The tool's language map lists around 110 codes, covering most widely used languages and many regional ones: English, Spanish, French, German, Portuguese, Italian, Dutch, Russian, Ukrainian, Polish, Czech, Romanian, Greek, Turkish, Arabic, Hebrew, Persian, Urdu, Hindi, Bengali, Punjabi, Gujarati, Marathi, Tamil, Telugu, Kannada, Malayalam, Sinhala, Nepali, Chinese (Simplified and Traditional), Japanese, Korean, Vietnamese, Thai, Indonesian, Malay, Filipino, Swahili, Amharic, Yoruba, Zulu, Afrikaans and others.

Coverage is not the same as certainty. Closely related languages share vocabulary and syntax, and the model may return a neighbour rather than the exact variety. A short phrase in Portuguese can score as Spanish, and a sentence in Norwegian can score as Danish. The confidence figure is your guide: the lower it is, the more the result should be treated as a best guess.

How to use the detector

1. Type or paste at least a few words into the text box. The character counter updates as you type, and the Detect button stays disabled until there are at least three characters.
2. Optionally press Load AI Model and wait for it to report that the model is ready.
3. Press Detect Language.
4. Read the result card: a percentage, the language name and the language code.
5. If the result looks wrong, paste a longer sample and detect again.
6. Press Clear to empty the box and reset the result.

Reading the confidence score

The tool groups the score into three bands. Above 80 percent it labels the detection as high confidence. Between 50 and 80 percent it suggests adding more text. Below 50 percent it warns that the result is weak. These bands are presentation aids, not guarantees: a high score on a two-word brand name can still be wrong, and a medium score on a long paragraph can be correct.

Use the score as a signal about how much evidence the model found, not as a measure of how much you should trust the tool in general. When two languages are plausible, the second-ranked language is often the useful clue, which is why a longer sample helps.

Getting a better result

• Use sentences, not single words. One word is often shared across languages; a full sentence gives the model grammar and function words to work with.
• Avoid names, codes and numbers. Product names, URLs and serial numbers have no language signal and can pull the prediction toward whichever language they resemble.
• Keep the sample in one language. Mixed-language text produces a mix of signals; the model still returns one top answer, which may be misleading.
• Paste plain text. Strip HTML, timestamps and formatting if you can; they add noise.
• Repeat with a different passage. If two samples disagree, the text may be short, mixed or genuinely ambiguous.

What it does not do

It does not translate, transliterate, correct spelling or identify dialects with certainty. It does not read romanised text reliably: Hindi or Urdu written in Latin letters often looks like English or another Latin-script language to the model, because the model sees the characters, not the intended language. It also does not return every candidate language in the interface; it shows the top one, even when the runner-up is close.

Privacy and network activity

Your text stays in the browser. The network requests the page makes are for the tool's own code, the MediaPipe runtime, the WebAssembly files and the model file; none of them contains your paragraph. The browser may cache those files, so a later visit can skip the download, although clearing the cache or a hard reload will fetch them again. As with any page, the server logs the request for the page itself, but not the text you type into the box.

Common mistakes

• Testing with one word. Short inputs are the main cause of surprising results.
• Assuming a low score means failure. It means the evidence is thin; add text.
• Expecting romanised Hindi to be detected as Hindi. Latin-script input is scored as a Latin-script language.
• Pressing Detect before the model loads and assuming it is broken. The loading panel shows progress; wait for it to finish.
• Treating the tool as a translator. It identifies; it does not convert.

Limitations

The detector works on the text you paste, not on audio or images, and its accuracy depends on sample length and language similarity. It reports one top language rather than a full ranking, so close calls are hidden. The model and runtime are downloaded from third-party hosts, which means the first use requires those hosts to be reachable. It cannot detect a language that the model was not trained on, and it cannot tell you which dialect or regional variety is in use.

Frequently Asked Questions