Text Cleanup Workflow
Polish messy text into clean, formatted output in 5 steps
Messy text is a constant problem — pasted content from documents, exported data with inconsistent formatting, or logs that need cleaning before use. This workflow walks you through five tools in the right order to get clean, consistent text every time.
Count characters and check length
Before cleaning text, get a baseline count so you can track what changes and catch truncation issues early.
Find and replace unwanted patterns
Most dirty text has repeated noise — extra spaces, wrong punctuation, or placeholder strings that need swapping out.
Remove duplicate lines
Exported data and logs often contain repeated entries that inflate size and create confusion downstream.
Fix letter casing
Inconsistent casing — ALL CAPS, random capitalization, or mixed case — makes text harder to read and process.
Convert to HTML for publishing
Plain text loses structure when pasted into HTML — line breaks disappear and special characters break the markup.
Pro Tips
- Work through the steps in order — cleaning before deduplication avoids missing duplicates caused by trailing spaces.
- Save your intermediate output after each step by copying to a text file, so you can backtrack if a step changes too much.
- For CSV or tabular data, use Find & Replace to swap delimiters before running duplicate removal.