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Smartly bulk parse unstructured addresses to extract names, phone numbers, states, cities, and detailed addresses, boosting your data processing efficiency.
One address per line, automatically extracts name, phone, province, city, region, detail address, zip code, etc.
Paste multiple address texts to start batch parsing
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Dealing with large volumes of poorly formatted shipping addresses and manually extracting names, phone numbers, and address levels is both time-consuming and error-prone. This tool uses natural language processing (NLP) and address recognition algorithms to automatically parse unstructured text into structured fields: name, phone number, province/state, city, district/county, and detailed address. Shipping address parsing is the process of identifying and separating individual address components from free-form text. Our tool supports various input formats, including single-line, multi-line, and mixed text, with no pre-formatting required.
Q: What input formats does the address parser support?
A: It supports single-line, multi-line, punctuated or unpunctuated text, and mixed text with names, phone numbers, and addresses in any order. For example, "Zhang San 13800138000 No. 10 Jiuxianqiao Road, Chaoyang District, Beijing" can be parsed directly.
Q: What fields are included in the parsed results?
A: The output includes 6 standard fields: name, mobile number, province/state, city, district/county, and detailed address, ensuring compatibility with most CRM and ERP systems.
Please do not upload excessively large batches or address data containing sensitive information. Parsing accuracy is affected by the completeness and standardization of the input, so we recommend manually verifying critical results. The tool does not store user data, but you should always be mindful of network transmission security.
For optimal address parsing, we recommend keeping the three-level administrative information (province/state, city, district) intact in your input. Avoid using abbreviations or nicknames (e.g., using "Imperial Capital" instead of Beijing), as standard names significantly improve recognition accuracy. A typical input like "Li Si 13912345678 No. 9999 Shennan Avenue, Nanshan District, Shenzhen City, Guangdong Province" will be structurally separated into name, phone number, province, city, district, and street details.
