About This Tool
Pull every email address out of a block of text in one click. Paste raw text, HTML source code, CSV data, or any document content, and this tool finds all email addresses using pattern matching, then lists them for you to copy or download. Manually scanning through a long document, webpage source, or data export to pick out email addresses is tedious and error-prone. You might miss addresses buried in HTML tags, overlook duplicates, or accidentally copy partial addresses. This tool automates the entire process: it identifies valid email patterns, removes duplicates when enabled, sorts results alphabetically, and lets you filter by domain to isolate addresses from a specific provider. The extraction uses a pattern based on the RFC 5322 email format specification. It handles standard addresses like name@company.com as well as addresses with plus tags (user+tag@domain.com), subdomains (user@mail.department.company.co.uk), and hyphens in both the local part and domain. Your data stays completely private and is never sent to any server, making this safe for processing text that contains personal or confidential information. Common uses include cleaning up contact lists exported from CRM systems, extracting addresses from email threads for mailing list creation, pulling contacts from website HTML source code, and parsing log files or CSV exports where email addresses are mixed with other data.
How the Email Extraction Works
The tool applies a regular expression pattern to your input text. This pattern matches sequences that follow the standard email format: a local part (before the @ sign), the @ symbol, and a domain part (after the @ sign) with at least one dot and a two-character or longer top-level domain.
The local part matches letters, numbers, dots, underscores, percent signs, plus signs, and hyphens. The domain part matches letters, numbers, dots, and hyphens, followed by a dot and a TLD of two or more letters. This covers the vast majority of real-world email addresses including formats like john.doe+newsletter@sub.example.co.uk.
After extraction, the deduplication step compares addresses case-insensitively. The email spec treats the local part as case-sensitive, but in practice almost every mail server treats User@Example.com and user@example.com as the same address. The tool keeps the first occurrence and removes later duplicates.
The domain filter checks whether the domain portion of each address contains your filter string. Entering "gmail" matches gmail.com addresses. Entering ".edu" matches all educational institution domains. The filter is case-insensitive and performs substring matching for flexibility.
Supported Email Formats
The extraction pattern recognizes these common email formats:
- Standard: user@domain.com
- Plus addressing: user+tag@domain.com (used for email filtering)
- Subdomains: user@mail.department.company.com
- Country code TLDs: user@company.co.uk, user@example.com.br
- Hyphens: first-last@my-company.com
- Numbers: user123@domain456.com
- Dots in local part: first.middle.last@domain.com
The pattern does not match IP-based addresses (user@[192.168.1.1]) or quoted local parts ("user name"@domain.com) since these are extremely rare in real-world usage and often indicate test or spam addresses.
Tips for Better Results
HTML source: When extracting from a webpage, right-click and "View Page Source" or "Inspect Element," then copy the raw HTML. Email addresses in mailto: links, hidden form fields, and embedded scripts will all be captured.
CSV and spreadsheet data: Copy the entire spreadsheet contents (select all, copy) and paste directly. The tool ignores commas, tabs, and other delimiters, focusing only on email-pattern matches.
Large text blocks: The tool handles text up to several megabytes without issues. For very large datasets (100,000+ emails), a command-line tool or dedicated desktop application may be faster.
Domain filtering: Use the domain filter after extraction to isolate specific groups. For example, filter by "company.com" to find only internal addresses, or filter by "gmail.com" to separate personal accounts from business accounts.
Cleaning results: If the extracted list contains false positives (strings that look like emails but are not), copy the results to a spreadsheet where you can manually review and remove invalid entries.
Privacy and Compliance Considerations
Email addresses are personal data under GDPR, CCPA, and similar privacy regulations. Extracting emails from publicly available sources is generally permitted, but sending unsolicited commercial messages to those addresses may violate anti-spam laws.
CAN-SPAM Act (US): Requires a clear unsubscribe mechanism, physical mailing address, and accurate subject lines in commercial emails. Penalties reach $51,744 per violation.
GDPR (EU): Requires a lawful basis for processing personal data. Legitimate interest may apply for B2B outreach, but you must provide opt-out mechanisms and respect data subject rights.
CASL (Canada): Requires express consent before sending commercial electronic messages, with limited exceptions for business inquiries.
Your data stays completely private when using this tool. No email addresses are transmitted, stored, or accessible to anyone except you. However, how you use the extracted data is your responsibility. Always obtain proper consent before adding addresses to mailing lists.