What email patterns are detected?

Standard RFC 5322 shapes — local@domain.tld — including dots, hyphens, plus-tags, and underscores in the local part. Obfuscated forms like "name (at) domain" are not detected.

Can I filter by domain?

Yes. After extraction, filter the list to a specific domain (e.g. only @example.com addresses) or sort by domain to group results.

Does it deduplicate addresses?

Yes. Dedupe is optional but enabled by default — repeated addresses appear once in the output. Sorting is also optional.

Are extracted addresses sent anywhere?

No. Scanning and dedupe happen entirely in your browser. Addresses are never logged or transmitted.

regex extract · dedupe · sort · domain filter

Extract email addresses from any text

Paste prose, HTML, logs, or contact dumps. Get a clean list of every email — deduped, sorted, lowercase-normalized.

Email Extractor Workspace

Input size

150 B

3 lines

Emails found

deduped

Filter

(none)

domain

Normalize

lower

case

input · any text/HTML

output · emails

info@example.com
support@example.com
jane.doe@acme.co.uk
bob@acme.co.uk
someone@gmail.com
other@yahoo.com

Quick Start

How to extract emails

Regex-based extraction with sensible defaults — deduplicate, normalize to lowercase, optionally filter by domain.

01
Paste text
Anything — HTML source, contact dumps, log files, scraped pages.
Tokenisation is Unicode-aware via \p{L}\p{N} property classes — so accented letters, ideographs, and emoji code points all count correctly.
anyUTF-8Unicodecode pointslive
input · textraw · UTF-8
```
info@example.com
```
loaded · UTF-8UTF-8 · ready
02
Set options
Dedupe (default on), sort, lowercase normalize, optional domain filter.
Live re-counts run on every keystroke through a memoised reducer, so even multi-megabyte inputs stay smooth in the editor.
dedupesortlivememoisedoptions
options · optionsconfigured · live
```
@example.com
```
options applied · livelive
03
Review matches
One per line. Counts shown in stats.
Output preserves the original line endings (\n vs \r\n) and trailing whitespace is reported separately so you can spot diff-only changes.
countline endingswhitespacediff-cleanreproducible
matches · countedlist · ready
```
info@example.com
support@example.com
```
counted · livecounted
04
Copy or download
Text or CSV (with header row).
Copy writes via the Async Clipboard API; downloads ship as text/plain; charset=utf-8 — nothing leaves your tab.
csvclipboardtext/plainblobclient-side
export · copiedready · done
```
✓ Copied
```
shipped · ready to shipsuccess

Pro tips

Text-processing fundamentals worth knowing

Tokenise with Unicode classes

Use \p{L}\p{N} with the u flag so accents, ideographs, and emoji count properly.

Normalise line endings

Convert \r\n to \n before diffing or hashing — Windows-vs-Unix endings produce phantom changes.

Mind invisible characters

Zero-width joiners, BOMs, and non-breaking spaces look identical to ASCII space. Strip them before comparison or storage.

Stream don’t slurp

For multi-megabyte text, iterate line-by-line rather than loading everything to memory — keeps the tab responsive.

Choose collation deliberately

Default JS sort is code-point order, not human alphabetical. Use Intl.Collator for locale-aware sorting.

Re-run after normalisation

NFC vs NFD changes byte length but not visible glyphs. String.prototype.normalize() before counts and comparisons.

Use Cases

When to extract emails

Anywhere a list of addresses needs harvesting.

Contact lists

Pull emails from CSV exports or vCards. Tokenisation uses Unicode property classes (\p{L}, \p{N}) with the u flag so accents, ideographs, and emoji code points all count correctly. Surrogate-pair-aware counts mean 🚀 weighs 1, not 2.

Unicode-correct

HTML scraping

Extract from copied web pages. Result updates as you type — paste and watch totals settle in milliseconds even on multi-megabyte inputs. Heavy work runs through a memoised reducer so the editor never janks.

diff-clean

Log analysis

Find email addresses in audit logs. Output normalises line endings to LF for clean diffs across platforms. Windows-vs-Unix CRLF mismatches disappear, which keeps git blame honest and review comments focused on real changes.

instant counts

PDF excerpts

Pull addresses from extracted PDF text. Code-point counts are stable across NFC/NFD normalisation. Pair with the Reading Time Calculator to layer scan-time on top of raw counts, useful for editorial budgets and SEO meta-description limits.

platform-aware

Bulk emailing

Build mailing lists from collected data. All processing runs client-side — paste sensitive or pre-publish copy without it leaving the tab. The clipboard API writes UTF-8 text/plain so it survives transit to any editor or CMS.

code-point-accurate

Compliance audit

Find PII in datasets that shouldn't have them. Same input plus same options always produces identical output, so the tool is safe to embed in CI scripts or doc-generation pipelines without race conditions.

lossless

FAQ

Frequently asked questions

What email format does it match?

Standard RFC-5322ish (simplified): local@domain.tld with allowed special characters.

Does it validate emails?

No — just extracts strings that look like emails. Use a real validator for delivery confidence.

Does the domain filter match subdomains?

Yes — filter 'example.com' matches user@mail.example.com.

Is anything sent to a server?

No. All processing runs locally.

What about obfuscated emails?

Standard 'at' or 'dot' obfuscation isn't decoded — only literal @ matches.

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