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Smartly extract structured information like province, city, district, street, name, phone number, and ID from raw text for data entry and cleaning.
Supports pasting mixed text containing name, phone, province/city/district address, automatically extracting key information.
Paste address text to start parsing
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When dealing with unstructured text containing a mix of addresses, names, phone numbers, and ID numbers, manually splitting the information is both time-consuming and prone to errors. This tool automatically identifies and extracts structured fields from text using a built-in address database and regular expression matching rules. The "shipping address" it processes refers to a continuous string of text containing administrative divisions, detailed addresses, and personal identification information. The output consists of separated fields such as province, city, district/county, street, zip code, name, phone number, and ID number.
Q: Is there a specific order required for the input text?
A: There is no strict order requirement. For example, both "John Doe 13800138000 No. 1 Jianguomenwai Avenue, Chaoyang District, Beijing 110105199001011234" and "No. 1 Jianguomenwai Avenue, Chaoyang District, Beijing 110105199001011234 John Doe 13800138000" can be parsed correctly.
Q: Can the tool parse addresses in Hong Kong, Macau, Taiwan, or overseas?
A: No. This tool primarily parses based on the administrative division database and phone/ID number rules of mainland China, making it suitable only for shipping addresses within mainland China.
Please ensure the input text contains sufficient information (such as province, city, and district). Text that is too brief (e.g., only "XX Road No. XX") may lead to parsing failure or reduced accuracy. This tool processes data locally and will not upload or store any of your input data. The tool performs format validation for ID numbers and phone numbers, but it cannot verify their authenticity. For batch processing, please process entries one by one.
To improve parsing accuracy, we recommend keeping the input information as complete as possible. A typical input/output example is as follows: Input "Keji South 12th Road, Science and Technology Park, Nanshan District, Shenzhen City, Guangdong Province Li Si 13800138000 440301199001011234 518057". The output will include: Province "Guangdong Province", City "Shenzhen City", District "Nanshan District", Street "Keji South 12th Road, Science and Technology Park", Name "Li Si", Phone Number "13800138000", ID Number "440301199001011234", and Zip Code "518057". For address data cleaning in e-commerce or CRM systems, you can first use this tool to standardize non-standard address text before proceeding with subsequent address verification or regional statistics.
