Automatic Language Detection

Collect raw text from user input, files, or APIs. Use libraries like PyMuPDF or Tika to extract text from PDFs, and BeautifulSoup for HTML. Ensure text is clean and free of encoding issues.

Tokenize text into n-grams (character or word) to capture language-specific patterns. Use character n-grams (e.g., 2-5 grams) as they are robust for short texts. Normalize by lowercasing and removing punctuation.

Load a pre-trained language detection model (e.g., fastText, langdetect, or a custom classifier). Feed the feature vector to predict language ISO code. For high accuracy, use a model trained on 100+ languages.

Why fastText: fastText is explicitly designed for language identification and text classification, directly matching the step's need for language classification inference.

Check if confidence score exceeds a threshold (e.g., 0.6). If low, fall back to a secondary model or heuristic (e.g., character set detection via chardet). For ambiguous texts, return 'unknown' or prompt user.

Map predicted language code to human-readable name (e.g., 'en' → 'English'). Optionally add metadata like script (Latin, Cyrillic) and region. Store result in structured format (JSON) for downstream use.

Return result to user or system via API, log file, or UI. For batch processing, write results to CSV or database. Optionally trigger downstream actions (e.g., route text to language-specific translator).