We all take our names for granted and yet there is so much information in it. Your name reveals a lot of information about who you are. It tells if you're a man or a women, what language you speak and it can even reveal your nationality.

Validating a phone number, zip code or email address is simple. You don’t need a parsing service for that. But parsing a name and getting information out of it is much harder. That is where we come in: Name Parser is an API service that splits a complete name into useful information such as first name, last name, gender and nationality. It makes your live as a developer or data scientist much easier. Just give us a name and we'll give you the useful components.

Order of names

Names can be written in different name orders . This makes it complicated for developers to check if a name is valid or not. The order [first name] [last name] is known as the Western order and is usually used in most Western countries (Europe, North and South America, India and Oceania). The order [last name] [first name] is known as the Eastern order and is primarily used in East Asia. Our software can parse all common name orders, salutations and titles.

Types Format Example
Regular names. [first name] [last name] Jennifer Anderson
Names with middle name. [first name] [middle name] [last name] Stephanie Karen Hills
Last name first. [last name], [first name] [middle name] Procházka, Lukáš Michal
Names with salutation and initials. [salutation] [first name] [initial] [last name] Mr Bob S. Samuels
Title with last name first. [title] [last name], [first name] Dr. Jenkins, Philip
Nickname between [], (), {} or "". [first name] [nickname] [last name] Stephanie "DJ" Williams
Cyrillic, Greek and Latin characters. [first name] [last name] Екатерина Иванов

Supporting 104 countries

In order to properly parse names, our service uses an huge database with 1.597.154 first names and 5.012.607 last names from 104 different countries. A significant part of this name data is available for purchase as a CSV file with names and gender at Name Census .

There are many names such as Robin, Pascal or Mickey, which are mainly used for girls in one country and for boys in other countries. By specifying the country code in the query, we can determine the gender of a name with greater certainty.

Country Names Country Names
United Arab Emirates United Arab Emirates 8.131 Afghanistan Afghanistan 4.143
Albania Albania 3.077 Argentina Argentina 14.963
Austria Austria 15.215 Australia Australia 19.283
Azerbaijan Azerbaijan 1.809 Bosnia and Herzegovina Bosnia and Herzegovina 3.657
Bangladesh Bangladesh 6.024 Belgium Belgium 26.068
Bulgaria Bulgaria 5.763 Bermuda Bermuda 2.777
Brazil Brazil 107.674 Belarus Belarus 1.951
Canada Canada 26.142 Switzerland Switzerland 58.506
Chile Chile 4.751 Cameroon Cameroon 4.004
China China 8.379 Colombia Colombia 10.011
Costa Rica Costa Rica 4.293 Czech Republic Czech Republic 8.081
Germany Germany 34.731 Denmark Denmark 130.994
Dominican Republic Dominican Republic 4.517 Algeria Algeria 3.283
Ecuador Ecuador 3.832 Egypt Egypt 4.676
Spain Spain 56.913 Finland Finland 4.831
France France 42.245 United Kingdom United Kingdom 54.136
Georgia Georgia 4.189 Ghana Ghana 3.775
Greece Greece 5.176 Guatemala Guatemala 4.156
Hong Kong Hong Kong 4.312 Honduras Honduras 2.763
Croatia Croatia 2.702 Hungary Hungary 8.362
Indonesia Indonesia 16.187 Ireland Ireland 8.890
Israel Israel 3.494 India India 23.161
Iraq Iraq 2.665 Iran Iran 3.683
Italy Italy 25.788 Jersey Jersey 4.101
Jamaica Jamaica 10.104 Jordan Jordan 1.915
Japan Japan 7.154 Kenya Kenya 5.041
South Korea South Korea 5.274 Kazakhstan Kazakhstan 2.262
Lebanon Lebanon 2.732 Liberia Liberia 5.473
Lithuania Lithuania 1.676 Luxembourg Luxembourg 3.472
Latvia Latvia 1.832 Morocco Morocco 3.651
Macedonia Macedonia 2.518 Mexico Mexico 14.176
Malaysia Malaysia 9.007 Nigeria Nigeria 8.875
Nicaragua Nicaragua 1.880 Netherlands Netherlands 21.799
Norway Norway 31.178 Nepal Nepal 3.593
New Zealand New Zealand 6.733 Panama Panama 2.564
Peru Peru 5.731 Philippines Philippines 11.770
Pakistan Pakistan 7.213 Poland Poland 53.376
Palestine Palestine 1.851 Portugal Portugal 16.901
Paraguay Paraguay 2.234 Romania Romania 6.554
Serbia Serbia 3.204 Russia Russia 34.491
Saudi Arabia Saudi Arabia 3.730 Sweden Sweden 35.910
Singapore Singapore 8.572 Slovenia Slovenia 9.472
Slovakia Slovakia 4.340 Somalia Somalia 1.630
El Salvador El Salvador 2.032 Syria Syria 2.640
Thailand Thailand 6.065 Tunisia Tunisia 2.044
Turkey Turkey 166.098 Taiwan Taiwan 3.712
Tanzania Tanzania 2.196 Ukraine Ukraine 6.645
Uganda Uganda 2.433 United States United States 105.958
Uruguay Uruguay 1.864 Saint Vincent Saint Vincent 3.078
Venezuela Venezuela 8.818 Vietnam Vietnam 3.403
Kosovo Kosovo 2.849 Mayotte Mayotte 14.873
Zambia Zambia 2.570 Zimbabwe Zimbabwe 2.601

Use cases

Name Parser is a blazing fast RESTful API that returns JSON objects. It is easy to integrate into any existing or new project. Names are a very common field in most online forms, databases and processes. To get an idea of the possibilities of our API we listed a few typical use cases.

1) Simplify contact forms

Almost all business websites contain a form where a visitor can enter his name. On average forms have five different text inputs like: first name, last name, salutation, email and country. Using our name parsing endpoint you can bring that back by 60% and even get more information. Instead of a user having to fill five fields, a visitor only has to enter his complete name and email address.

  • Shorten contact, registration and order forms by 60%
  • Get more conversions (people don't like to fill in long forms)
  • Get more information out of your form like gender and nationality
2) Validate names

Do you have a website where people can sign up, order or submit a question via a contact form? You can use our name validation endpoint to check if a name exists, is not made up or misspelled. In addition the validation endpoint also detects if a name is possibly fake by comparing it with 13.738 famous, fictional and humorous names.

  • Make sure you get high quality sign-ups
  • Validate if a name is not fake
  • Check if a name is is not misspelled
3) Clean up your existing customer database

Do you already have an existing database with customer names? Use our name parsing endpoint to flag misspelled or fake names an enrich your database with additional customer information. Our paid plans offer the possibility to process a high volume of names in a short time.

  • Validate your customer names
  • Detect misspelled and fake names
  • Get the right salutation for your emails and other communication
  • Enrich your database with your customers gender, possible nationality
4) Generate fictional names for your development databases

In most cases when you develop software, apps or web projects you work with names of customers. For new projects you can generate fictional accounts including the salutation, gender and country details. The endpoint is not only useful for new projects but also for existing projects. Instead of having a development database with real names it's more secure to work with fictional names instead. Use our name generator endpoint to generate a database with fictional names for any country code.

  • Generate fictional user accounts (name, gender, country) for new projects
  • Improve the security of your applications by developing with fictional names
5) Extract names from text

Do you want extract names from news articles, form messages or web pages? Use our name extraction endpoint to extract complete names from any given text.

  • Extract names from news articles or social media posts for automated tagging
  • Extract names from web pages like "About us" or "Meet the Team".
  • Search and replace extracted names from emails to mask names for GDPR compliance.


We always keep on improving our service. We divided the changelog in a section for the API and for the database. Our API returns the version of the API and database via the response headers. You can use the versions from the response header to programmatically detect if we release a new version.

Version Date Changes
1.4 13-11-2020
  • Added the name extraction endpoint to the API to extract names from text.
  • Created database with common keywords in every supported language to reduce false possive names for extraction endpoint.
  • Examined and improved complete API to meet strict GDPR regulations.
1.3 30-09-2020
  • All endpoints don't try to parse (obvious) rubbish names anymore.
  • Added additional status codes for when names could not be found (404).
  • Improved the rate limiting functionality excluding error responses.
  • Added additional hourly and daily usage fields to account information endpoint.
  • Parse and Validation endpoints accept different orders for first and last name like: last name, first name first name or last name first name.
1.2 01-06-2020
  • Added rate limiting based upon subscription plan.
  • Improved the name parser so it can detect multiple middle names.
  • Increased accuracy of nationality prediction using new algorithm
  • Updated the structure of the result object to a more logical model.
  • Added support for IP addresses to Country Code using MaxMind GeoLite2.
  • Added Pantheon and IMDB data files for loose and strict validation.
1.1 24-09-2019
  • Fixed salutation breaking after a title.
  • Added the detection of nicknames that are written like: (nickname), [nickname] or "nickname".
  • Added the following endpoints to the API: account information, validation and name generation.
1.0 17-08-2019
  • Released first version of the API.
Version Date Changes
7 07-10-2020
  • Updated countries RU in the gender database.
  • Created a lastname table to import all official lastnames.
  • Added all supported countries to lastname database.
  • In the countries RU, PL, CZ the lastname also indicates the gender.
  • Updated firstname database for countries CA, RU, PL, IT, CH with additional names.
6 31-08-2020
  • Exported 10.187.493 public profiles to rebuilt the first name database resulting in 122 countries.
  • Improved performance of cron jobs that collect public profiles.
  • Fixed the Russian first names entries.
5 05-05-2020
  • Names from unsupported countries are now added and based upon first names from supported countries resulting in 110 countries.
  • Started measuring and tracking nationality prediction performance by using open database with names and countries from 29,216 olympic athletes .
  • Updated country tables with additional country information.
  • Added countries HU, SI, SK, IN, RU and UA.
4 26-12-2019
  • Added column country_rank to name tables to support nationality prediction.
  • Added countries AR, BR, AU, NZ, CZ, ES, FR, GR, PL, TR to the gender database.
3 22-10-2019
  • Updated tables, now including frequency of names.
  • Added additional table for ASCII version of each name.
  • Added countries AT, CA, DK, IT, NO, IE, CH, PT to the gender database.
2 02-09-2019
  • Released first version of the database.
  • Converted whole database to UTF-8 when importing name databases.
  • Added countries NL, SE, DE, FI, BE, US, GB to the gender database.
1 17-08-2019
  • Released first version of the database.