Parse usable Street Address, City, State, Zip from a string [closed]

Answer 1

The original poster has likely long moved on, but I took a stab at porting the Perl Geo::StreetAddress:US module used by geocoder.us to C#, dumped it on CodePlex, and think that people stumbling across this question in the future may find it useful:

US Address Parser

On the project's home page, I try to talk about its (very real) limitations. Since it is not backed by the USPS database of valid street addresses, parsing can be ambiguous and it can't confirm nor deny the validity of a given address. It can just try to pull data out from the string.

It's meant for the case when you need to get a set of data mostly in the right fields, or want to provide a shortcut to data entry (letting users paste an address into a textbox rather than tabbing among multiple fields). It is not meant for verifying the deliverability of an address.

It doesn't attempt to parse out anything above the street line, but one could probably diddle with the regex to get something reasonably close--I'd probably just break it off at the house number.

Answer 2

Since there is chance of error in word, think about using SOUNDEX combined with LCS algorithm to compare strings, this will help a lot !

Answer 3

I've done a lot of work on this kind of parsing. Because there are errors you won't get 100% accuracy, but there are a few things you can do to get most of the way there, and then do a visual BS test. Here's the general way to go about it. It's not code, because it's pretty academic to write it, there's no weirdness, just lots of string handling.

(Now that you've posted some sample data, I've made some minor changes)

1. Work backward. Start from the zip code, which will be near the end, and in one of two known formats: XXXXX or XXXXX-XXXX. If this doesn't appear, you can assume you're in the city, state portion, below.
2. The next thing, before the zip, is going to be the state, and it'll be either in a two-letter format, or as words. You know what these will be, too -- there's only 50 of them. Also, you could soundex the words to help compensate for spelling errors.
3. before that is the city, and it's probably on the same line as the state. You could use a zip-code database to check the city and state based on the zip, or at least use it as a BS detector.
4. The street address will generally be one or two lines. The second line will generally be the suite number if there is one, but it could also be a PO box.
5. It's going to be near-impossible to detect a name on the first or second line, though if it's not prefixed with a number (or if it's prefixed with an "attn:" or "attention to:" it could give you a hint as to whether it's a name or an address line.

I hope this helps somewhat.

Answer 4

This type of problem is hard to solve because of underlying ambiguities in the data.

Here is a Perl based solution that defines a recursive descent grammar tree based on regular expressions to parse many valid combination of street addresses: http://search.cpan.org/~kimryan/Lingua-EN-AddressParse-1.20/lib/Lingua/EN/AddressParse.pm . This includes sub properties within an address such as: 12 1st Avenue N Suite # 2 Somewhere CA 12345 USA

It is similar to http://search.cpan.org/~timb/Geo-StreetAddress-US-1.03/US.pm mentioned above, but also works for addresses that are not from the USA, such as the UK, Australia and Canada.

Here is the output for one of your sample addresses. Note that the name section would need to be removed first from "A. P. Croll & Son 2299 Lewes-Georgetown Hwy, Georgetown, DE 19947" to reduce it to "2299 Lewes-Georgetown Hwy, Georgetown, DE 19947". This is easily achieved by removing all data up to the first number found in the string.

Non matching part       ''
Error                   '0'
Error descriptions      ''
Case all                '2299 Lewes-Georgetown Hwy Georgetown DE 19947'
COMPONENTS              ''
country                 ''
po_box_type             ''
post_box                ''
post_code               '19947'
pre_cursor              ''
property_identifier     '2299'
property_name           ''
road_box                ''
street                  'Lewes-Georgetown'
street_direction        ''
street_type             'Hwy'
sub_property_identifier ''
subcountry              'DE'
suburb                  'Georgetown'

Answer 5

I've been working in the address processing domain for about 5 years now, and there really is no silver bullet. The correct solution is going to depend on the value of the data. If it's not very valuable, throw it through a parser as the other answers suggest. If it's even somewhat valuable you'll definitely need to have a human evaluate/correct all the results of the parser. If you're looking for a fully automated, repeatable solution, you probably want to talk to a address correction vendor like Group1 or Trillium.

Answer 6

RecogniContact is a Windows COM object that parses US and European addresses. You can try it right on http://www.loquisoft.com/index.php?page=8