libpostal 0,0,2,0,3,2,7,0 travis-ci

A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.

libpostal: international street address NLP

Build Status License OpenCollective OpenCollective

🇧🇷 🇫🇮 🇳🇬 :jp: 🇽🇰 🇧🇩 🇵🇱 🇻🇳 🇧🇪 🇲🇦 🇺🇦 🇯🇲 :ru: 🇮🇳 🇱🇻 🇧🇴 :de: 🇸🇳 🇦🇲 :kr: 🇳🇴 🇲🇽 🇨🇿 🇹🇷 :es: 🇸🇸 🇪🇪 🇧🇭 🇳🇱 :cn: 🇵🇹 🇵🇷 :gb: 🇵🇸

libpostal is a C library for parsing/normalizing street addresses around the world using statistical NLP and open data. For a more comprehensive overview of the research, check out the introductory blog post, but to sum up, the goal of this project is to understand location-based strings in every language, everywhere.

🇷🇴 🇬🇭 🇦🇺 🇲🇾 🇭🇷 🇭🇹 :us: 🇿🇦 🇷🇸 🇨🇱 :it: 🇰🇪 🇨🇭 🇨🇺 🇸🇰 🇦🇴 🇩🇰 🇹🇿 🇦🇱 🇨🇴 🇮🇱 🇬🇹 :fr: 🇵🇭 🇦🇹 🇱🇨 🇮🇸 🇮🇩 🇦🇪 🇸🇰 🇹🇳 🇰🇭 🇦🇷 🇭🇰

Addresses and the locations they represent are essential for any application dealing with maps (place search, transportation, on-demand/delivery services, check-ins, reviews). Yet even the simplest addresses are packed with local conventions, abbreviations and context, making them difficult to index/query effectively with traditional full-text search engines. This library helps convert the free-form addresses that humans use into clean normalized forms suitable for machine comparison and full-text indexing. Though libpostal is not itself a full geocoder, it can be used as a preprocessing step to make any geocoding application smarter, simpler, and more consistent internationally.

The core library is written in pure C. Language bindings for Python, Ruby, Go, Java, PHP, and NodeJS are officially supported and it's easy to write bindings in other languages.


If your company is using libpostal, consider asking your organization to sponsor the project and help fund our continued research into geo + NLP. Interpreting what humans mean when they refer to locations is far from a solved problem, and sponsorships help us pursue new frontiers in machine geospatial intelligence. As a sponsor, your company logo will appear prominently on the Github repo page along with a link to your site. Sponsorship info


Individual users can also help support open geo NLP research by making a monthly donation:

Examples of parsing

libpostal implements the first statistical address parser that works well internationally, trained on ~50 million addresses in over 100 countries and as many languages. We use OpenStreetMap (anything with an addr:* tag) and the OpenCage address format templates at: to construct the training data, supplementing with containing polygons and perturbing the inputs in a number of ways to make the parser as robust as possible to messy real-world input.

These example parse results are taken from the interactive address_parser program that builds with libpostal when you run make. Note that the parser is robust to commas vs. no commas, casing, different permutations of components (if the input is e.g. just city or just city/postcode).


The parser achieves very high accuracy on held-out data, currently 98.9% correct full parses (meaning a 1 in the numerator for getting every token in the address correct).

Usage (parser)

Here's an example of the parser API using the Python bindings:

from postal.parser import parse_address
parse_address('The Book Club 100-106 Leonard St Shoreditch London EC2A 4RH, United Kingdom')

And an example with the C API:

#include <stdio.h>
#include <stdlib.h>
#include <libpostal/libpostal.h>

int main(int argc, char **argv) {
    // Setup (only called once at the beginning of your program)
    if (!libpostal_setup() || !libpostal_setup_parser()) {

    address_parser_options_t options = get_libpostal_address_parser_default_options();
    address_parser_response_t *parsed = parse_address("781 Franklin Ave Crown Heights Brooklyn NYC NY 11216 USA", options);

    for (size_t i = 0; i < parsed->num_components; i++) {
        printf("%s: %s\n", parsed->labels[i], parsed->components[i]);

    // Free parse result

    // Teardown (only called once at the end of your program)

Examples of normalization

The expand_address API converts messy real-world addresses into normalized equivalents suitable for search indexing, hashing, etc.

Here's an interactive example using the Python binding:


libpostal contains an OSM-trained language classifier to detect which language(s) are used in a given address so it can apply the appropriate normalizations. The only input needed is the raw address string. Here's a short list of some less straightforward normalizations in various languages.

Input Output (may be multiple in libpostal)
One-hundred twenty E 96th St 120 east 96th street
C/ Ocho, P.I. 4 calle 8 polígono industrial 4
V XX Settembre, 20 via 20 settembre 20
Quatre vingt douze R. de l'Église 92 rue de l' église
ул Каретный Ряд, д 4, строение 7 улица каретныи ряд дом 4 строение 7
ул Каретный Ряд, д 4, строение 7 ulitsa karetnyy ryad dom 4 stroyeniye 7
Marktstrasse 14 markt straße 14

libpostal currently supports these types of normalizations in 60+ languages, and you can add more (without having to write any C).

For further reading and some bizarre address edge-cases, see: Falsehoods Programmers Believe About Addresses.

Usage (normalization)

Here's an example using the Python bindings for succinctness (most of the higher-level language bindings are similar):

from postal.expand import expand_address
expansions = expand_address('Quatre-vingt-douze Ave des Champs-Élysées')

assert '92 avenue des champs-elysees' in set(expansions)

The C API equivalent is a few more lines, but still fairly simple:

#include <stdio.h>
#include <stdlib.h>
#include <libpostal/libpostal.h>

int main(int argc, char **argv) {
    // Setup (only called once at the beginning of your program)
    if (!libpostal_setup() || !libpostal_setup_language_classifier()) {

    size_t num_expansions;
    normalize_options_t options = get_libpostal_default_options();
    char **expansions = expand_address("Quatre-vingt-douze Ave des Champs-Élysées", options, &num_expansions);

    for (size_t i = 0; i < num_expansions; i++) {
        printf("%s\n", expansions[i]);

    // Free expansions
    expansion_array_destroy(expansions, num_expansions);

    // Teardown (only called once at the end of your program)


Before you install, make sure you have the following prerequisites:

On Ubuntu/Debian

sudo apt-get install curl libsnappy-dev autoconf automake libtool pkg-config


sudo yum install snappy snappy-devel autoconf automake libtool pkgconfig

On Mac OSX

brew install snappy autoconf automake libtool pkg-config

Then to install the C library:

git clone
cd libpostal
./configure --datadir=[...some dir with a few GB of space...]
sudo make install

# On Linux it's probably a good idea to run
sudo ldconfig

libpostal has support for pkg-config, so you can use the pkg-config to print the flags needed to link your program against it:

pkg-config --cflags libpostal         # print compiler flags
pkg-config --libs libpostal           # print linker flags
pkg-config --cflags --libs libpostal  # print both

For example, if you write a program called app.c, you can compile it like this:

gcc app.c `pkg-config --cflags --libs libpostal`


Libpostal is designed to be used by higher-level languages. If you don't see your language of choice, or if you're writing a language binding, please let us know!

Officially supported language bindings

Unofficial language bindings

Database extensions

Unofficial REST API

Libpostal REST Docker

Command-line usage (expand)

After building libpostal:

cd src/

./libpostal "Quatre vingt douze Ave des Champs-Élysées"

If you have a text file or stream with one address per line, the command-line interface also accepts input from stdin:

cat some_file | ./libpostal --json

Command-line usage (parser)

After building libpostal:

cd src/


address_parser is an interactive shell. Just type addresses and libpostal will parse them and print the result.


libpostal uses greatest for automated testing. To run the tests, use:

make check

Adding test cases is easy, even if your C is rusty/non-existent, and we'd love contributions. We use mostly functional tests checking string input against string output.

libpostal also gets periodically battle-tested on tens of millions of addresses from OSM (clean) as well as anonymized queries from a production geocoder (not so clean). During this process we use valgrind to check for memory leaks and other errors.

Data files

libpostal needs to download some data files from S3. The basic files are on-disk representations of the data structures necessary to perform expansion. For address parsing, since model training takes about a day, we publish the fully trained model to S3 and will update it automatically as new addresses get added to OSM. Same goes for the language classifier model.

Data files are automatically downloaded when you run make. To check for and download any new data files, run:

libpostal_data download all $YOUR_DATA_DIR/libpostal

And replace $YOUR_DATA_DIR with whatever you passed to configure during install.

Language dictionaries

libpostal contains a number of per-language dictionaries that influence expansion, the language classifier, and the parser. To explore the dictionaries or contribute abbreviations/phrases in your language, see resources/dictionaries.


  • Abbreviation expansion: e.g. expanding "rd" => "road" but for almost any language. libpostal supports > 50 languages and it's easy to add new languages or expand the current dictionaries. Ideographic languages (not separated by whitespace e.g. Chinese) are supported, as are Germanic languages where thoroughfare types are concatenated onto the end of the string, and may optionally be separated so Rosenstraße and Rosen Straße are equivalent.

  • International address parsing: sequence model which parses "123 Main Street New York New York" into {"house_number": 123, "road": "Main Street", "city": "New York", "state": "New York"}. The parser works for a wide variety of countries and languages, not just US/English. The model is trained on > 50M OSM addresses, using the templates in the OpenCage address formatting repo to construct formatted, tagged traning examples for most countries around the world. Many types of normalizations are performed to make the training data resemble real messy geocoder input as closely as possible.

  • Language classification: multinomial logistic regression trained on all of OpenStreetMap ways, addr:* tags, toponyms and formatted addresses. Labels are derived using point-in-polygon tests in Quattroshapes and official/regional languages for countries and admin 1 boundaries respectively. So, for example, Spanish is the default language in Spain but in different regions e.g. Catalunya, Galicia, the Basque region, the respective regional languages are the default. Dictionary-based disambiguation is employed in cases where the regional language is non-default e.g. Welsh, Breton, Occitan. The dictionaries are also used to abbreviate canonical phrases like "Calle" => "C/" (performed on both the language classifier and the address parser training sets)

  • Numeric expression parsing ("twenty first" => 21st, "quatre-vingt-douze" => 92, again using data provided in CLDR), supports > 30 languages. Handles languages with concatenated expressions e.g. milleottocento => 1800. Optionally normalizes Roman numerals regardless of the language (IX => 9) which occur in the names of many monarchs, popes, etc.

  • Fast, accurate tokenization/lexing: clocked at > 1M tokens / sec, implements the TR-29 spec for UTF8 word segmentation, tokenizes East Asian languages chracter by character instead of on whitespace.

  • UTF8 normalization: optionally decompose UTF8 to NFD normalization form, strips accent marks e.g. à => a and/or applies Latin-ASCII transliteration.

  • Transliteration: e.g. улица => ulica or ulitsa. Uses all CLDR transforms, the exact same source data as used by ICU, though libpostal doesn't require pulling in all of ICU (might conflict with your system's version). Note: some languages, particularly Hebrew, Arabic and Thai may not include vowels and thus will not often match a transliteration done by a human. It may be possible to implement statistical transliterators for some of these languages.

  • Script detection: Detects which script a given string uses (can be multiple e.g. a free-form Hong Kong or Macau address may use both Han and Latin scripts in the same address). In transliteration we can use all applicable transliterators for a given Unicode script (Greek can for instance be transliterated with Greek-Latin, Greek-Latin-BGN and Greek-Latin-UNGEGN).


  • Geographic name aliasing (coming soon): New York, NYC and Nueva York alias to New York City. Uses the crowd-sourced GeoNames ( database, so alternate names added by contributors can automatically improve libpostal.

  • Geographic disambiguation (coming soon): There are several equally likely Springfields in the US (formally known as The Simpsons problem), and some context like a state is required to disambiguate. There are also > 1200 distinct San Franciscos in the world but the term "San Francisco" almost always refers to the one in California. Williamsburg can refer to a neighborhood in Brooklyn or a city in Virginia. Geo disambiguation is a subset of Word Sense Disambiguation, and attempts to resolve place names in a string to GeoNames entities. This can be useful for city-level geocoding suitable for polygon/area lookup. By default, if there is no other context, as in the San Francisco case, the most populous entity will be selected.

  • Ambiguous token classification (coming soon): e.g. "dr" => "doctor" or "drive" for an English address depending on the context. Multiclass logistic regression trained on OSM addresses, where abbreviations are discouraged, giving us many examples of fully qualified addresses on which to train.


  • Verifying that a location is a valid address
  • Street-level geocoding

Raison d'être

libpostal was originally created as part of the OpenVenues project to solve the problem of venue deduping. In OpenVenues, we have a data set of millions of places derived from terabytes of web pages from the Common Crawl. The Common Crawl is published monthly, and so even merging the results of two crawls produces significant duplicates.

Deduping is a relatively well-studied field, and for text documents like web pages, academic papers, etc. there exist pretty decent approximate similarity methods such as MinHash.

However, for physical addresses, the frequent use of conventional abbreviations such as Road == Rd, California == CA, or New York City == NYC complicates matters a bit. Even using a technique like MinHash, which is well suited for approximate matches and is equivalent to the Jaccard similarity of two sets, we have to work with very short texts and it's often the case that two equivalent addresses, one abbreviated and one fully specified, will not match very closely in terms of n-gram set overlap. In non-Latin scripts, say a Russian address and its transliterated equivalent, it's conceivable that two addresses referring to the same place may not match even a single character.

As a motivating example, consider the following two equivalent ways to write a particular Manhattan street address with varying conventions and degrees of verbosity:

  • 30 W 26th St Fl #7
  • 30 West Twenty-sixth Street Floor Number 7

Obviously '30 W 26th St Fl #7 != '30 West Twenty-sixth Street Floor Number 7' in a string comparison sense, but a human can grok that these two addresses refer to the same physical location.

libpostal aims to create normalized geographic strings, parsed into components, such that we can more effectively reason about how well two addresses actually match and make automated server-side decisions about dupes.

So it's not a geocoder?

If the above sounds a lot like geocoding, that's because it is in a way, only in the OpenVenues case, we have to geocode without a UI or a user to select the correct address in an autocomplete dropdown. Given a database of source addresses such as OpenAddresses or OpenStreetMap (or all of the above), libpostal can be used to implement things like address deduping and server-side batch geocoding in settings like MapReduce or stream processing.

Now, instead of trying to bake address-specific conventions into traditional document search engines like Elasticsearch using giant synonyms files, scripting, custom analyzers, tokenizers, and the like, geocoding can look like this:

  1. Run the addresses in your database through libpostal's expand_address
  2. Store the normalized string(s) in your favorite search engine, DB, hashtable, etc.
  3. Run your user queries or fresh imports through libpostal and search the existing database using those strings

In this way, libpostal can perform fuzzy address matching in constant time relative to the size of the data set.

Why C?

libpostal is written in C for three reasons (in order of importance):

  1. Portability/ubiquity: libpostal targets higher-level languages that people actually use day-to-day: Python, Go, Ruby, NodeJS, etc. The beauty of C is that just about any programming language can bind to it and C compilers are everywhere, so pick your favorite, write a binding, and you can use libpostal directly in your application without having to stand up a separate server. We support Mac/Linux (Windows is not a priority but happy to accept patches), have a standard autotools build and an endianness-agnostic file format for the data files. The Python bindings, are maintained as part of this repo since they're needed to construct the training data.

  2. Memory-efficiency: libpostal is designed to run in a MapReduce setting where we may be limited to < 1GB of RAM per process depending on the machine configuration. As much as possible libpostal uses contiguous arrays, tries (built on contiguous arrays), bloom filters and compressed sparse matrices to keep memory usage low. It's conceivable that libpostal could even be used on a mobile device, although that's not an explicit goal of the project.

  3. Performance: this is last on the list for a reason. Most of the optimizations in libpostal are for memory usage rather than performance. libpostal is quite fast given the amount of work it does. It can process 10-30k addresses / second in a single thread/process on the platforms we've tested (that means processing every address in OSM planet in a little over an hour). Check out the simple benchmark program to test on your environment and various types of input. In the MapReduce setting, per-core performance isn't as important because everything's being done in parallel, but there are some streaming ingestion applications at Mapzen where this needs to run in-process.

C codebase

libpostal is written in modern, legible, C99 and uses the following conventions:

  • Roughly object-oriented, as much as allowed by C
  • Almost no pointer-based data structures, arrays all the way down
  • Uses dynamic character arrays (inspired by sds) for safer string handling
  • Confines almost all mallocs to name_new and all frees to name_destroy
  • Efficient existing implementations for simple things like hashtables
  • Generic containers (via klib) whenever possible
  • Data structrues take advantage of sparsity as much as possible
  • Efficient double-array trie implementation for most string dictionaries
  • Tries to stay cross-platform as much as possible, particularly for *nix

Python codebase

The geodata package in the libpostal repo is a confederation of scripts for preprocessing the various geo data sets and building input files for the C lib to use during model training. Said scripts shouldn't be needed for most users unless you're rebuilding data files for the C lib.

Address parser accuracy

On held-out test data (meaning labeled parses that the model has not seen before), the address parser achieves 98.9% full parse accuracy.

For some tasks like named entity recognition it's preferable to use something like an F1 score or variants, mostly because there's a class bias problem (most tokens are non-entities, and a system that simply predicted non-entity for every token would actually do fairly well in terms of accuracy). That is not the case for address parsing. Every token has a label and there are millions of examples of each class in the training data, so accuracy is preferable as it's a clean, simple and intuitive measure of performance.

Here we use full parse accuracy, meaning we only give the parser a "point" in the numerator if it gets every single token in the address correct. That should be a better measure than simply looking at whether each token was correct.

Improving the address parser

Though the current parser works quite well for most standard addresses, there is still room for improvement, particularly in making sure the training data we use is as close as possible to addresses in the wild. There are four primary ways the address parser can be improved even further (in order of difficulty):

  1. Contribute addresses to OSM. Anything with an addr:housenumber tag will be incorporated automatically into the parser next time it's trained.
  2. If the address parser isn't working well for a particular country, language or style of address, chances are that some name variations or places being missed/mislabeled during training data creation. Sometimes the fix is to add more countries at:, and in many other cases there are relatively simple tweaks we can make when creating the training data that will ensure the model is trained to handle your use case without you having to do any manual data entry. If you see a pattern of obviously bad address parses, the best thing to do is post an issue to Github.
  3. We currently don't have training data for things like apartment/flat numbers. The tags are fairly uncommon in OSM and the address-formatting templates don't use floor, level, apartment/flat number, etc. This would be a slightly more involved effort, but would be worth starting a discussion.
  4. We use a greedy averaged perceptron for the parser model primarily for its speed and relatively good performance compared to slower, fancier models. Viterbi inference using a linear-chain CRF may improve parser performance on certain classes of input since the score is the argmax over the entire label sequence not just the token. This may slow down training significantly although runtime performance would be relatively unaffected.


Bug reports and pull requests are welcome on GitHub at


The software is available as open source under the terms of the MIT License.

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