Email Extractor
Extract email addresses from pasted text, deduplicate them, count domains, and copy results as a list or CSV.
Cleaning email lists from pasted text
Paste meeting notes, exported contact text, support logs, or page copy to pull out email addresses without manually scanning every line. The list output is easiest for quick copy, while CSV output works better for spreadsheets and lightweight CRM cleanup.
Extraction only finds addresses present in the text you paste. Review the result before using it for outreach, remove personal data you do not need, and respect consent and anti-spam rules for your jurisdiction.
About the Email extractor
The email extractor pulls every email address out of a block of text and hands you a clean, deduplicated list. Paste in a signature dump, a forwarded thread, a scraped page, or a spreadsheet column, and it finds anything shaped like name@domain.tld, drops the duplicates, and shows you a per-domain breakdown alongside the count.
It solves the tedious job of hunting through messy text for contact details by hand. Reach for it when you have addresses tangled inside prose or markup and you want them as a tidy column for a mailing list, a CRM import, or a quick audit of which domains a list is weighted toward. Everything runs in your browser, so the addresses never leave your device.
How to use it
- Paste or type the text containing email addresses into the input box on the left.
- Leave Lowercase normalization on to fold Ada@Example.com and ada@example.com into one entry, or turn it off to keep original casing.
- Toggle Sort alphabetical to order the results, which makes scanning and diffing two lists easier.
- Read the live count badges and the domain summary to see how many unique addresses and domains were found.
- Click Copy list for one address per line, or Copy CSV to get an email,domain table ready for a spreadsheet.
Examples
Deduplicating a signature dump
Paste 'Contact Ada at Ada@Example.com or support@example.com.' With lowercase on, the extractor returns ada@example.com and support@example.com as two unique entries. The domain summary shows example.com with a count of 2, telling you both contacts share one domain.
Building a CSV for import
Drop in a long forwarded thread with thirty mixed addresses. The tool dedupes them to the real unique set, then Copy CSV produces rows like "jane@acme.io","acme.io". You paste that straight into a spreadsheet, with the domain column already split out for filtering.
Frequently asked questions
- Does it find every possible email format?
- It matches the common shape: letters, digits, dots, and the symbols ._%+- before the @, then a domain with a two-letter-or-longer top-level suffix. Plus-addressing like user+tag@gmail.com works. Exotic quoted local parts or brand-new long TLDs may be missed.
- Are duplicate addresses removed automatically?
- Yes. Results pass through a Set, so each address appears once. With lowercase normalization on, casing differences such as Ada@Example.com and ada@example.com collapse into a single entry before deduplication.
- What does the domain count tell me?
- It groups the unique addresses by the part after the @ and tallies how many you have per domain. That is handy for spotting that a list is mostly gmail.com, or for separating internal company addresses from external ones.
- Is my text uploaded anywhere?
- No. Extraction happens entirely in your browser with a regular expression. Nothing is sent to a server, so you can safely paste private or internal contact data.
- Why is the CSV quoted?
- Each field is wrapped in double quotes and internal quotes are doubled, which keeps the file valid even if an address contains a comma. Spreadsheets and import tools parse that quoting correctly.
Good to know
Extraction is pattern based, not verification based. The tool confirms that a string looks like an address, not that the mailbox exists or accepts mail. Before sending a real campaign, run the list through a verification service to catch typos, dead domains, and spam traps.
A regex will occasionally grab something that is technically an email pattern but not a real contact, for example an example.com placeholder in documentation or a tracking address. Skim the output before you rely on it. If you only want addresses from one organisation, the domain summary is the fastest way to confirm the list is clean, and sorting alphabetically makes it easy to spot near-duplicates that differ by a single character.