How to Extract Email Addresses From Text (Any Format) | TitleCasePro
Learn how to extract email addresses from plain text, HTML, CSV files, and logs instantly — and which patterns count as valid emails.
Quick answer: Paste any block of text into the email extractor and it will find every email address instantly — from plain-text documents, HTML, logs, CSVs, or email threads — and give you a clean, deduplicated list you can copy or export as CSV.
Whether you are cleaning up a contact list, pulling addresses out of an exported spreadsheet, or scanning a log file for user accounts, extracting email addresses by hand is slow and error-prone. An automated extractor handles it in under a second.
What Text Formats Work
The extractor does not care about formatting. It scans the raw text character by character and identifies patterns that match the email address structure. This means it works on:
- Plain text — paste a copied email thread, document, or note
- HTML source — paste raw page markup; the regex ignores tags and finds addresses in
href="mailto:..."attributes and body text - CSV or TSV exports — paste the raw file content, including headers
- Log files — application logs often contain email addresses in error messages and user-action records
- Markdown files — contact information in README files, link text, and footnotes
What Counts as a Valid Email Address
The extractor uses the practical RFC-5322 pattern that covers all real-world email formats:
local-part @ domain . tldWhere:
- local-part can contain letters, digits, dots (
.), underscores (_), percent signs (%), plus signs (+), and hyphens (-) - domain is a hostname with optional subdomains
- tld is at least two characters
This matches:
| Example | Valid? |
|---|---|
user@example.com | ✓ |
user+filter@example.com | ✓ (plus addressing) |
user@mail.company.co.uk | ✓ (subdomain) |
contact@startup.io | ✓ (new TLD) |
@username | ✗ (no domain) |
user@ | ✗ (no TLD) |
All results are lowercased for consistency — Hello@EXAMPLE.COM and hello@example.com are treated as the same address when deduplication is on.
Handling Duplicates
When collecting from multiple sources or a long document, duplicate addresses are common. The extractor’s Remove duplicates toggle keeps only the first occurrence of each address in the order they appear. This preserves the original sequence rather than arbitrarily picking one copy.
To alphabetise the results — useful when merging two lists — turn on Sort A → Z before copying.
Stripping Trailing Punctuation
Text often places email addresses inside punctuation:
Contact alice@example.com.— trailing period from end of sentenceSend to bob@example.com,— trailing comma from a list(support@site.org)— inside parentheses
The extractor strips trailing periods, commas, semicolons, and closing parentheses from the end of each match so you get the clean address, not alice@example.com. with the period attached.
Exporting as CSV
The Copy as CSV button exports the results as a single-column CSV with a header row:
email
alice@example.com
bob@test.orgThis format imports directly into Excel, Google Sheets, Mailchimp, HubSpot, and most other tools without any additional reformatting.
Privacy
The extractor runs entirely in your browser. Your text is never sent to any server, logged, or stored. This makes it safe for sensitive contact data, private documents, and internal communications.
Related Tools
- Extract URLs From Text — pull all links out of the same text
- Text Cleaner — remove line breaks and extra spaces before extracting
- Word Counter — count words, characters, and reading time
Related Guides
- How to Extract URLs From Text — Extract all links alongside email addresses
- How to Clean Up Messy Text — Clean your source text before extracting emails
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