Raw text is messy. Cleaning it improves search, analytics and model training.
Encoding
Fix mis-encoded characters (mojibake), normalise Unicode forms, and standardise quotes and whitespace.
Remove Boilerplate
Strip navigation menus, cookie banners, footers, signatures and repeated headers from scraped or exported text.
Markup
Convert HTML to text or Markdown, keeping meaningful structure such as headings, lists and tables.
Language Detection
Identify languages to route or filter documents.
Quality Filters
For training data, filter out very short or very long documents, gibberish, spam, repeated text and low-information pages. Heuristics and classifiers both help.
Sensitive Content
Detect and redact personal data and secrets; filter harmful content where appropriate.
Keep Originals
Store raw data alongside cleaned versions so cleaning rules can be changed later.
Don't Over-Clean
Lowercasing, removing punctuation or stripping numbers can destroy meaning that modern models use. Match cleaning to the downstream use.