Normalizing Page Text for Spellchecking

Now that we've covered Building a Webpage Spellchecker, our Django app can send text to LanguageTool and get spelling feedback.

But raw webpage text is messy.

A page is not just paragraphs. It can include:

• navigation labels
• cookie banners
• JavaScript snippets
• CSS text
• repeated footer links
• weird whitespace
• hidden content
• product names and URLs

If we send all of that directly into a spellchecker, we get noisy results. A startup engineer cares about this because noisy reports reduce user trust.

In this lesson, we'll build the normalization layer between the scraper and the spellchecker:

scraped HTML
   ↓
extract visible text
   ↓
clean spacing and entities
   ↓
preserve useful context
   ↓
spellcheck-ready text

In the next lesson, we'll turn these normalized results into a user-facing audit report UI.