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Copy and paste any text containing email addresses - web pages, documents, logs, customer support tickets, contact lists, or scraped data. Tool scans entire text for valid email patterns.
Enable 'Remove duplicates' to get unique emails only, eliminating repeated addresses. Enable 'Convert to lowercase' for consistent formatting (user@EXAMPLE.com β user@example.com).
See extracted email addresses displayed individually with count. Validates email format: requires @ symbol, domain name, and valid TLD (.com, .org, .uk, etc).
Copy as line-separated list (one per line) for spreadsheets, or comma-separated CSV for direct import into CRM, mailing lists, or contact management systems.
Understanding email address extraction
Email extraction uses regex patterns to find and parse email addresses from unstructured text, identifying valid addresses matching format: local-part@domain.tld. Developers, marketers, and data analysts extract emails from web scraping results, customer support logs, contact forms, business documents, and social media data to build mailing lists, analyze customer communications, or migrate contact databases.
Email addresses follow RFC 5322 standard but practical implementations use simplified regex matching alphanumeric characters, dots, hyphens, underscores before @ symbol, followed by domain name and top-level domain (TLD). Valid emails include john.doe@example.com, support+tag@company.co.uk, user_name@sub.domain.org. Invalid formats like @test.com (missing local part) or user@ (missing domain) are rejected.
Lead Generation: Marketing teams extract emails from website scraping, LinkedIn profiles, or conference attendee lists to build prospecting databases. Automated extraction faster than manual copying. Clean, deduplicated email lists improve campaign deliverability and reduce bounce rates.
Customer Support Analysis: Support teams analyze ticket archives to identify frequently contacting customers, extract CCs from email threads, or build contact databases from historical communications. Batch extract emails from thousands of support tickets for sentiment analysis or response time metrics.
Data Migration: Moving from one CRM/email system to another requires extracting emails from exported data files, CSV exports, or database dumps. Email extractor handles unstructured formats where contacts embedded in text fields, notes, or concatenated strings.
Contact List Cleanup: Deduplicate mailing lists by extracting all emails from various sources (spreadsheets, documents, databases) into single list, then remove duplicates. Normalize email formatting (lowercase, trim whitespace) for consistent database storage.
Web Scraping Post-Processing: After scraping contact pages, "about us" sections, or directory listings, raw HTML contains emails mixed with markup, JavaScript, and styling. Extract valid emails from scraped content, discard HTML noise.
GDPR Compliance Auditing: Data privacy regulations require knowing what personal data your organization holds. Extract emails from codebases, configuration files, logs, and documentation to identify where email addresses are stored for GDPR data mapping and deletion requests.
This tool uses regex pattern: /[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}/g
Local Part (before @): Alphanumeric characters, dots, underscores, percent signs, plus signs, hyphens. Most common email formats supported. Excludes special chars like brackets, quotes which are technically valid but rarely used.
Domain Part (after @): Alphanumeric characters and hyphens for domain name, dots for subdomains and TLD. Requires at least 2-character TLD (.com, .uk, .io). Supports internationalized domains using standard ASCII.
Limitations: Doesn't validate if email actually exists, just format validity. Accepts technically invalid but common formats. Doesn't support quoted strings or internationalized local parts. For production email validation, use server-side verification (SMTP check, email verification API).
Deduplication: Removes duplicate emails from extracted results. Case-insensitive comparison - User@Example.com equals user@example.com. Useful when text contains repeated mentions of same contacts.
Lowercase Normalization: Converts all emails to lowercase for consistency. Email addresses are case-insensitive per RFC but databases typically store lowercase for uniform querying. Prevents duplicate entries from case variations.
Format Output: Export as newline-separated list (one email per line) for spreadsheet import, or comma-separated values (CSV) for CRM imports, mailing list uploads, or database bulk inserts.
All processing happens client-side using JavaScript regex matching - your text never leaves your browser. Safe for extracting emails from confidential documents, customer data, or proprietary business information.
How developers use email extraction
Export LinkedIn connection data or Sales Navigator search results, paste into extractor to get clean email list. Remove duplicates, normalize formatting, then import into CRM for outbound campaigns.
// LinkedIn CSV export contains mixed format data:
Name,Company,Email,Notes
John Doe,Acme Inc,john.doe@acme.com,"Met at conference, discussed partnership, email: support@acme.com"
Jane Smith,Example Ltd,JANE@EXAMPLE.COM,"Follow up next quarter"
Bob Johnson,Test Corp,bob@test.com,"Also CC bob.johnson@personal.com on proposals"
// Traditional manual extraction: tedious, error-prone
// Copy each email individually, track duplicates manually
// Using Email Extractor:
// 1. Paste entire CSV/text
// 2. Enable "Remove duplicates" + "Lowercase"
// 3. Extract results:
// john.doe@acme.com
// support@acme.com
// jane@example.com
// bob@test.com
// bob.johnson@personal.com
// 4. Export as CSV for CRM import
// Saves hours on large lists (100+ contacts)
// Catches emails in Notes/Comments fields
// Normalizes formatting automaticallyAnalyze helpdesk exports or support ticket archives to identify high-frequency support requesters, extract all CCs from email threads, or build contact database from historical tickets.
// Support ticket text (Zendesk/Freshdesk export):
Ticket #12345
From: customer@example.com
CC: manager@example.com, team@example.com
Subject: Product inquiry
Body: Please contact me at customer.alt@gmail.com or my colleague: partner@acme.com
Ticket #12346
From: customer@example.com
Subject: Follow-up question
Body: Can you also send details to billing@example.com?
// Extract all emails from ticket archive:
// Results:
// - customer@example.com (appears twice - dedupe)
// - manager@example.com
// - team@example.com
// - customer.alt@gmail.com
// - partner@acme.com
// - billing@example.com
// Use cases:
// - Identify VIP customers (frequent senders)
// - Build complete contact database
// - Find all email aliases for same customer
// - Export for email campaign targeting
// - GDPR compliance: locate all stored emailsAfter web scraping company contact pages, 'about us' sections, or staff directories, extract valid emails from HTML markup, JavaScript, and text noise. Get clean list from messy scraped data.
// Raw scraped HTML from contact page:
<div class="contact-info">
<p>Email us at: <a href="mailto:info@company.com">info@company.com</a></p>
<script>var email = "sales" + "@" + "company.com";</script>
<span>Support: support@company.com</span>
<!-- Obfuscated: admin [at] company [dot] com -->
<p>Press inquiries: press@company.com or media@company.com</p>
<div style="display:none">spam@trap.com</div>
</div>
// Paste entire scraped HTML into extractor
// Automatically finds:
info@company.com
support@company.com
press@company.com
media@company.com
spam@trap.com
// Filter out obvious spam traps manually
// Export clean list for lead database
// Benefits over manual copying:
// - Handles obfuscation (JavaScript email building)
// - Ignores HTML markup
// - Finds emails in hidden elements
// - Catches multiple emails per page
// - No risk of transcription errorsCombine mailing lists from different systems (old CRM, spreadsheets, email archives, business cards) into unified, deduplicated contact database. Extract emails from all sources, merge, clean.
// Source 1: Old CRM CSV export
Alice,alice@example.com,Active
Bob,BOB@test.com,Inactive
// Source 2: Business card notes
"Met Bob from Test Corp (bob@test.com) and
Carol from Acme (carol@acme.com) at conference"
// Source 3: Email signature scraping
Email archives contain: alice@example.com, dave@company.com
// Manual merging: track duplicates, normalize case
// Tedious and error-prone for 100+ contacts
// Email Extractor approach:
// 1. Paste all sources into single text block
// 2. Enable deduplication + lowercase
// 3. Extract results:
alice@example.com
bob@test.com
carol@acme.com
dave@company.com
// Clean list ready for new CRM import
// No duplicates (Bob appeared twice, different case)
// All formatting normalized
// Completed in seconds vs hours of manual workMaster email extraction
This tool scans text using regex pattern matching to identify valid email addresses, then applies filtering and formatting options. All processing happens client-side - your text never leaves your browser.
Paste any text containing email addresses: web pages, documents, CSV files, log files, support tickets, scraped HTML. Tool accepts unlimited text length and scans for patterns matching email format: local@domain.tld.
Regex pattern matches standard email formats: alphanumeric characters, dots, underscores, plus signs before @ symbol. Domain must include at least one dot and 2+ character TLD (.com, .org, .co.uk). Rejects invalid formats like @test, user@, or test@test (missing TLD).
Enable "Remove duplicates" to get unique email addresses only. Case-insensitive comparison treats user@example.com and USER@example.com as duplicates. If email appears multiple times in text (repeated contact mentions, CC lists, signature blocks), only one instance returned.
Useful for: building mailing lists from repeated contacts, cleaning up scraped data with duplicate entries, merging contact lists from multiple sources.
Enable "Convert to lowercase" to standardize email formatting. Email addresses are case-insensitive per RFC specification - User@EXAMPLE.COM delivers to same mailbox as user@example.com. Databases typically store lowercase emails for consistent querying and duplicate detection.
Prevents database duplicates from case variations. Recommended for: CRM imports, mailing list uploads, database storage, any system requiring normalized email format.
Line-Separated List: One email per line. Copy and paste into spreadsheet cells, text files, or scripts processing line-by-line. Format:
user1@example.com
user2@test.com
user3@acme.com
Comma-Separated (CSV): Emails separated by commas and spaces. Direct import into CRM systems, email marketing platforms (Mailchimp, SendGrid), or database bulk inserts. Format: user1@example.com, user2@test.com, user3@acme.com
Choose format matching your destination system's import requirements.
Everything you need to know
Your data never leaves your browser
Your text never leaves your browser. This email extractor operates entirely client-side using JavaScript regex pattern matching. Zero server uploads, zero data transmission, zero logging.
Safe for extracting emails from confidential customer data, proprietary business documents, support ticket archives, GDPR subject access requests, or any sensitive text containing personal information. Fully compliant with data privacy regulations.
Performance metrics