Remove Numbers or Letters from Text

Remove all numbers, letters, or punctuation from text online. Clean imported data, strip line numbers, or isolate numeric values.

🔒 Your text stays in your browser - nothing is sent to our servers
Input
0Characters
0No spaces
0Words
0Sentences
0Lines
< 1 minRead time
Result
How to Use

Three steps to get started

1

Paste your text

Paste any mixed text containing numbers, letters, or punctuation you want to remove.

2

Choose what to remove

Select "Remove Numbers," "Remove Letters," or "Remove Punctuation" depending on what you need to strip out.

3

Copy the cleaned text

The cleaned output appears instantly. Copy or download it.

About This Tool

Strip numbers, letters, or punctuation from any text

This is a character-class filter: it deletes every character belonging to one category - digits, letters, or punctuation - and leaves everything else exactly where it was. It does not reformat, re-wrap, or reorder anything, which is what makes it safe to run on structured data. The equivalent one-liners aretext.replace(/[0-9]/g, '') in JavaScript ortr -d '0-9' in a shell; this just saves you opening an editor.

The three modes and exactly what each one targets:

  • Remove Numbers - deletes the ten ASCII digits 0–9. Letters, spaces, punctuation and symbols survive, so Order #4471 shipped becomes Order # shipped
  • Remove Letters - deletes a–z and A–Z. Everything numeric and symbolic survives, so Item 42: $9.99 becomes 42: $9.99
  • Remove Punctuation - deletes every character that is neither alphanumeric nor whitespace, so quotes, dashes, brackets, slashes and currency signs all go

When you would actually reach for this

The recurring cases are cleanup jobs where the noise sits inside the useful content rather than around it:

  • Un-numbering a list pasted from a PDF or a code viewer, where 1., 2., 3. came along with the text
  • Pulling figures out of a report - strip the letters and you are left with the amounts, dates, and counts ready for a spreadsheet column
  • NLP and text-analysis preprocessing - tokenizers, word-frequency counts, and word clouds usually want punctuation gone before the split step
  • Normalizing product SKUs or model names that carry version digits you do not want in a display label
  • Getting text past a strict validator - form fields, filename rules, and legacy imports that reject symbols outright

Two behaviours to expect. First, removal leaves the gaps behind: deleting digits from Chapter 12 Introduction gives Chapter Introductionwith a double space, so chain the Remove Extra Spaces tool afterwards if the spacing matters. Second, Remove Letters is ASCII-scoped - accented letters such as é, ñ, ü and non-Latin scripts are not ina–z, so they survive; run Remove Accents first if you need them folded down to plain letters that this filter will then catch.

Every mode is a regex replace evaluated in your browser as you type. There is no upload, no length ceiling, and nothing is logged - you can paste an entire export and it never leaves the machine.

FAQ

Frequently Asked Questions

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