Rewrite Text

Sanitize datasets with custom mapping and whole-word logic. Apply recursive double-pass processing to clean whitespace. Normalize your data structure.

Input Text

Paste the text that should be rewritten.

Transformation Rules

Enter one rewrite rule per line in the format "from=to". Leave the right side empty to delete text. Use "\n" to insert a newline.

Rewrite Only Full Words

Apply the rules only when the match is a standalone whole word.

Clean-up Whitespace

Trim each line and collapse repeated spaces after rewriting removes text.

Case Sensitive Rules

Only apply rules when uppercase and lowercase letters match exactly.

Double Rewriting

Run the full rewrite pass twice so rewritten text can be processed again.

Rewritten text:

Please configure parameters and execute the action.

About Rewrite Text

Rewrite Text applies custom transformation rules to letters, words, or larger text fragments. You can rewrite only whole words, clean leftover whitespace, enforce case-sensitive matching, and run the rewriting pass twice if needed.

How It Works

Use the tool in three quick steps:

Paste the source text - Add the text that should be rewritten.
Enter transformation rules - Write one rule per line in the format "from=to".
Generate rewritten text - Click Rewrite Text to apply the transformations.

Basic Examples

Rewrite letters and words

Input Text:
Roses are red.

Transformation Rules:
R=p
red=blue

Output:
poses are blue.

Rewrite only full words

Input Text:
app appetite app

Transformation Rules:
app=tool

Rewrite Only Full Words:
checked

Output:
tool appetite tool

Delete symbols and clean whitespace

Input Text:
this  & that

Transformation Rules:
&=

Clean-up Whitespace:
checked

Output:
this that

Real-World Usage Scenarios

Legacy-Data-Normalization - Database administrators often use these rules to update old product prefixes or internal IDs across large datasets. By setting rules like 'SKU2020=PROD2024', they can quickly align legacy text with new organizational standards without manual editing.
Code-Snippet-Refactoring - Developers use the 'Rewrite Only Full Words' option to rename variables or functions in script snippets. This prevents accidental partial matches—ensuring that a rule to change 'id' to 'userID' doesn't turn 'hidden' into 'huserIDden'.
Sensitive-Data-Anonymization - Professionals redacting logs or reports can remove sensitive identifiers by leaving the 'to' side of a rule empty. Setting 'InternalName=' effectively strips out private labels, while the 'Clean-up Whitespace' feature ensures the resulting text remains professional and readable.
Content-Template-Customization - Marketing teams use transformation rules to swap placeholders in email templates. By running a 'Double Rewriting' pass, they can first replace generic tags and then apply a second layer of formatting to the newly inserted text.

Frequently Asked Questions

How-do-I-delete-specific-words-from-my-text?

To remove text, enter the word or character you want to delete followed by an equals sign, leaving the right side empty (e.g., 'Draft='). This will strip all occurrences of that word from the output.

What-is-the-benefit-of-the-Double-Rewriting-feature?

Double Rewriting performs two consecutive passes of your rules. This is useful when a replacement word itself needs to be transformed by another rule in the same list, allowing for complex, multi-stage text processing.

Can-I-insert-line-breaks-during-the-rewrite?

Yes. Use '\n' in your transformation rule to represent a newline. For example, 'separator=\n\n' would replace the word 'separator' with two new lines to create vertical spacing.

Why-should-I-enable-Rewrite-Only-Full-Words?

This setting ensures that transformations only apply to standalone words. It prevents the tool from modifying fragments inside larger words, such as changing 'cat' in 'category' when you only intended to replace the animal name.

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