Address Parser
Parse any free-form address into structured components using machine learning. Supports international addresses in any format.
ML-Powered
Uses machine learning trained on billions of addresses worldwide.
International
Supports addresses from any country in any language or format.
Structured Output
Returns clean components: street, city, state, postal code, country.
Data Cleaning
Perfect for normalizing messy address data in your databases.
How Address Parsing Works
Normalization
The raw address is normalized by expanding abbreviations (St → Street, Ave → Avenue) and standardizing formatting across different input styles.
ML Classification
Our advanced ML model, trained on billions of addresses, classifies each token as street, city, country, postal code, or other components.
Component Extraction
The classified tokens are assembled into a structured output with separate fields for each address component.
Why Parse Addresses?
Data cleaning and normalization is essential when working with address data from multiple sources. User-entered addresses, imported spreadsheets, and legacy databases often have inconsistent formatting that needs standardization.
Geocoding preparation often requires structured address data. Before you can plot addresses on a map or calculate distances, you need clean components that geocoding services can understand.
Form validation and autocomplete systems benefit from understanding address structure. Parse partial inputs to suggest completions or validate that users have entered complete addresses.
Frequently Asked Questions
What address formats are supported?
We support virtually any address format from any country. The ML model was trained on billions of international addresses and handles variations in formatting, language, and structure.
Can it handle multilingual addresses?
Yes. The parser works with addresses in any language including those using non-Latin scripts like Chinese, Japanese, Korean, Arabic, Cyrillic, and more.
What about incomplete addresses?
The parser does its best with incomplete data. It will extract whatever components are present. Missing fields simply won't appear in the output rather than being filled with guesses.
Is this different from geocoding?
Yes. Address parsing extracts components from text. Geocoding converts addresses to coordinates. You'd typically parse first, then geocode. We offer both services via our API.