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Automatically identify and extract all valid email addresses from any text, with optional result counting.
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When you need to quickly gather contacts from massive amounts of text—such as customer service logs, forum posts, or code logs—manually searching for email addresses is both time-consuming and error-prone. This tool automatically scans your input text to accurately identify and extract all email addresses that match standard formats. An email address is a string containing an "@" symbol, a local part (username), and a valid domain name, serving as a fundamental identifier for internet communication. The tool uses regular expressions for pattern matching, outputting a plain text list of addresses, with an option to display the total extraction count at the top.
user@domain.com from invalid formats like user@@test.com or not-an-email@com, ensuring clean results.Q: What file formats does the Email Address Extractor support?
A: This tool currently only supports plain text input. You need to copy the text content from files like PDFs, Word documents, or images first, and then paste it into the tool for processing.
Q: How does the tool determine if an email address is successfully extracted?
A: The tool's core criterion is the address format. A typical valid email like name.123+tag@sub.example.co.uk will be extracted, while strings missing an "@" or having an incomplete domain will be ignored. For addresses containing special characters (like dots or plus signs), as long as the overall structure meets the standard, they will be correctly identified.
Please ensure the input content is plain text. The tool cannot directly parse text from binary files or images. The extraction results are based on common regular expressions, so a very small number of custom or non-standard email formats (such as those containing rare top-level domains or special encoding) may not be recognized. All processing is done locally in your browser, and no data is uploaded to our servers. However, for privacy protection, it is still recommended to avoid processing highly sensitive or confidential information. The length of text processed at one time is limited by your browser's memory, but it is usually sufficient for daily needs.
For data cleaning or web scraper data pre-processing scenarios, it is recommended to perform an initial cleanup of the raw text by removing excessive HTML tags or garbled text to improve extraction accuracy. The regular expression used by this tool primarily matches common email patterns. A typical input/output example is as follows: Input text "Contact support@tool.com and sales@example.net, invalid address like @abc.com", the output will be two independent lines of email addresses: support@tool.com and sales@example.net. If it encounters strings with many "@" symbols that are not emails (such as certain code snippets), the tool will filter them out to ensure clean results.