Introduction

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 113 countries

In order to properly parse names, our service uses an huge database with 1.712.459 first names and 5.238.897 last names from 113 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	9.196		Afghanistan	4.881
	Albania	3.552		Angola	2.673
	Argentina	16.530		Austria	15.983
	Australia	20.831		Azerbaijan	2.148
	Bosnia and Herzegovina	3.951		Bangladesh	6.998
	Belgium	27.527		Burkina Faso	3.042
	Bulgaria	6.635		Bermuda	3.091
	Bolivia	2.659		Brazil	109.186
	Belarus	2.569		Canada	28.305
	Switzerland	58.942		Ivory Coast	2.759
	Chile	5.877		Cameroon	4.797
	China	9.321		Colombia	12.002
	Costa Rica	4.937		Cyprus	2.364
	Czech Republic	8.829		Germany	37.559
	Denmark	131.094		Dominican Republic	5.372
	Algeria	4.160		Ecuador	4.941
	Egypt	5.979		Spain	59.853
	Finland	5.588		France	44.949
	United Kingdom	56.508		Georgia	4.638
	Ghana	4.779		Greece	5.803
	Guatemala	4.951		Hong Kong	4.612
	Honduras	3.438		Croatia	3.029
	Hungary	9.030		Indonesia	18.446
	Ireland	9.715		Israel	3.871
	India	25.465		Iraq	3.450
	Iran	4.182		Italy	27.859
	Jersey	4.436		Jamaica	11.080
	Jordan	2.193		Japan	8.045
	Kenya	5.878		South Korea	5.998
	Kuwait	2.280		Kazakhstan	2.925
	Lebanon	3.136		Liberia	6.136
	Lithuania	1.780		Luxembourg	4.104
	Latvia	1.997		Morocco	4.110
	Macedonia	2.670		Mexico	17.285
	Malaysia	9.649		Nigeria	10.158
	Nicaragua	2.699		Netherlands	23.575
	Norway	31.505		Nepal	4.079
	New Zealand	7.203		Panama	3.307
	Peru	6.914		Philippines	13.577
	Pakistan	8.209		Poland	53.686
	Palestine	1.984		Portugal	17.511
	Paraguay	2.844		Qatar	3.110
	Romania	7.616		Serbia	3.613
	Russia	36.259		Saudi Arabia	4.576
	Sudan	1.978		Sweden	36.289
	Singapore	9.290		Slovenia	9.613
	Slovakia	4.621		Senegal	2.151
	Somalia	1.975		El Salvador	2.591
	Syria	2.880		Thailand	6.476
	Tunisia	2.312		Turkey	167.103
	Taiwan	3.991		Tanzania	2.661
	Ukraine	7.652		Uganda	3.117
	United States	110.693		Uruguay	2.504
	Saint Vincent	3.369		Venezuela	11.454
	Vietnam	3.723		Kosovo	3.368
	Mayotte	16.493		Zambia	2.816
	Zimbabwe	2.799

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
• Make sure that sign-ups use their business email.

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 the validate parameter 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.

Changelog

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.

API

Version	Date	Changes
1.6	2021-03-01	Updated ‘generate’ endpoint so each subscription type (including free plan) can receive 25 generated names per request using the 'results' parameter.
1.5	2021-02-12	Added ‘email‘ as a parameter to the name parsing endpoint to extract names from email. Added 'email' object to all response objects including provider and country information. Added fictional email addresses to the random name generator output. Updated ‘account’ endpoint so usage field always has 24 hours and 31 days. Removed the 'gender' endpoint from the API as this is already available in the name parsing endpoint. Moved the 'validation' endpoint to the name parsing endpoint and made it available as an additional parameter.
1.4	2020-11-13	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	2020-09-30	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	2020-06-01	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	2019-09-24	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	2019-08-17	Released first version of the API.

Database

Version	Date	Changes
8	2021-01-30	Exported 11.371.003 public profiles to rebuilt the first name and last name database resulting in 113 countries. Added PE (Peruvian) last name database. Added 'compact' ASCII column to first name and last name databases for search.
7	2020-10-07	Updated countries RU in the gender database. Created a last name table to import all official last names. Added all supported countries to last name database. In the countries RU, PL, CZ the last name also indicates the gender. Updated first name database for countries CA, RU, PL, IT, CH with additional names.
6	2020-08-31	Exported 9.252.364 public profiles to rebuilt the first name database resulting in 104 countries. Improved performance of cron jobs that collect public profiles. Fixed the Russian first names entries.
5	2020-05-05	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	2019-12-26	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	2019-10-22	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	2019-09-02	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	2019-08-17	Released first version of the database.