Text Language Detector

Our language detector uses the franc library, which employs statistical n-gram analysis to identify languages. It analyzes character patterns, common letter combinations (bigrams and trigrams), and compares them against known language profiles. This method is highly effective because each language has unique character frequency distributions and typical letter sequences. The tool supports over 80 languages including English, Chinese, Japanese, Korean, Arabic, Russian, and many European languages.

For optimal accuracy, we recommend providing at least 20-30 characters of text. Short phrases (under 20 characters) may produce unreliable results because there isn't enough linguistic data for the algorithm to analyze. The more text you provide, the more accurate the detection becomes. For languages with similar roots (like Spanish and Portuguese, or Danish and Norwegian), longer text samples help distinguish between them more effectively.

The tool supports 80+ languages including: English, Chinese (Mandarin), Spanish, French, German, Italian, Portuguese, Russian, Japanese, Korean, Arabic, Hindi, Turkish, Dutch, Swedish, Norwegian, Danish, Finnish, Polish, Ukrainian, Vietnamese, Thai, Indonesian, Malay, Bengali, Greek, Hebrew, Czech, Romanian, Hungarian, and many more. The full list covers most widely spoken languages across Europe, Asia, the Middle East, and beyond.

The detector identifies the predominant language in the text. If your text contains a mix of languages (for example, English with some French phrases), it will typically detect the language that makes up the majority of the content. For texts with significant code-switching or multilingual content, the result reflects the dominant language. For best results with mixed-language content, try separating the text into single-language segments.

Several factors can affect accuracy: (1) Text too short – very brief texts lack sufficient linguistic fingerprints. (2) Similar languages – closely related languages (e.g., Czech/Slovak, Danish/Norwegian) share many patterns. (3) Specialized content – text with many numbers, URLs, code snippets, or proper nouns can confuse the algorithm. (4) Transliterated text – text written in a non-native script (e.g., Arabic written in Latin letters) may be misidentified. For best results, use natural, complete sentences.

No. All language detection happens entirely in your browser using client-side JavaScript. Your text never leaves your device, is never sent to any server, and is not stored anywhere. This ensures complete privacy and also makes the tool work offline once the page is loaded. You can use this tool with confidence even with sensitive or confidential text content.

N-gram based detection works by breaking text into overlapping sequences of N characters (typically bigrams of 2 or trigrams of 3 characters). Each language has a characteristic distribution of these n-grams. For example, "th", "he", "in", "er" are very common in English, while "ch", "sch", "ei", "en" are frequent in German. The algorithm compares the n-gram profile of your input text against pre-computed profiles for each supported language and finds the closest match using statistical distance measures.

To get the most accurate results: (1) Provide at least 30+ characters of natural text. (2) Use complete sentences rather than isolated words or fragments. (3) Avoid including numbers, URLs, email addresses, or code within the text. (4) Use the original script of the language (e.g., use Cyrillic for Russian, not Latin transliteration). (5) For short texts, try to include common words or phrases unique to that language.