Parse an Address

ShipEngine can use machine learning and natural language processing (NLP) to parse addresses data from unstructured text using the /v1/addresses/recognize endpoint.

Data can often enter your system as unstructured text (for example: emails, SMS messages, support tickets, or other documents). ShipEngine's address recognition API saves you from parsing this text manually and trying to extract the useful data within it. Instead, you can send us the unstructured text and we'll return whatever address data it contains.

Our machine learning models learn and improve over time so they become more accurate and better at understanding different writing patterns.

Requirements

This endpoint is only available to accounts on the Advanced plan or higher.
ShipEngine NLP currently supports English text and can recognize addresses for the following countries:
- Australia
- Canada
- Ireland
- New Zealand
- United Kingdom
- United States

Address Parsing Use Case

Let's say you receive an order via email. You can send the text of the email to ShipEngine and it will automatically extract the customer's address. Here's an example:

I need to send a package to my friend Amanda Miller’s house at 525 Winchester Blvd in San Jose (that's california, obviously). The zip code there is 95128.

Send this information to ShipEngine using PUT /v1/addresses/recognize.

ShipEngine will recognize the following pieces of information:

Entity Type	Value
`person`	Amanda Miller
`address`	Amanda Miller 525 Winchester Blvd San Jose, CA 95128
`address_residential_indicator`	residential
`address_line1`	525 Winchester Blvd
`city_locality`	San Jose
`state_province`	CA
`postal_code`	95128

Example Request & Response

PUT /v1/addresses/recognize

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PUT /v1/addresses/recognize HTTP/1.1
Host: api.shipengine.com
API-Key: __YOUR_API_KEY_HERE__
Content-Type: application/json
{
  "text": "I need to send a package to my friend Amanda Millers house at 525 Winchester Blvd in San Jose (thats california, obviously). The zip code there is 95128."
}

Example Response

The response includes a score property with a decimal number to indicate level of confidence in the parsing accuracy. In this example, the response has an overall score of 0.971069... which indicates a 97% confidence that it parsed the text correctly. The score value can help your application programmatically decide if you will need any additional input or verification from your user.

The entities object breaks down the recognized data further into their own individual properties and provides additional scoring on the confidence for each.

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{
    "score": 0.9710697187242877,
    "address": {
        "name": "Amanda Miller",
        "address_line1": "525 Winchester Blvd",
        "city_locality": "San Jose",
        "state_province": "CA",
        "postal_code": "95128",
        "address_residential_indicator": "yes"
    },
    "entities": [
        {
            "type": "person",
            "score": 0.9519646137063122,
            "text": "Amanda Miller",
            "start_index": 38,
            "end_index": 50,
            "result": {
                "value": "Amanda Miller"
            }
        },
        {
            "type": "residential_indicator",
            "score": 0.9519646137063122,
            "text": "house",
            "start_index": 54,
            "end_index": 58,
            "result": {
                "value": "yes"
            }
        },
        {
            "type": "address_line",
            "score": 0.9805313966503588,
            "text": "525 Winchester Blvd",
            "start_index": 63,
            "end_index": 81,
            "result": {
                "line": 1,
                "value": "525 Winchester Blvd"
            }
        },
        {
            "type": "number",
            "score": 0.9805313966503588,
            "text": "525",
            "start_index": 63,
            "end_index": 65,
            "result": {
                "type": "cardinal",
                "value": 525
            }
        },
        {
            "type": "city_locality",
            "score": 0.9805313966503588,
            "text": "San Jose",
            "start_index": 86,
            "end_index": 93,
            "result": {
                "value": "San Jose"
            }
        },
        {
            "type": "state_province",
            "score": 0.9805313966503588,
            "text": "california",
            "start_index": 103,
            "end_index": 112,
            "result": {
                "name": "California",
                "value": "CA"
            }
        },
        {
            "type": "postal_code",
            "score": 0.9519646137063122,
            "text": "95128",
            "start_index": 149,
            "end_index": 153,
            "result": {
                "value": "95128"
            }
        }
    ]
}

Already-Known Fields

You can specify any already-known fields for your address in the request. This can help you automatically define any known variables you might collect, such as:

name
city_locality
state_province
postal_code
country_code

Example with Already-Known Fields

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PUT /v1/addresses/recognize HTTP/1.1
Host: api.shipengine.com
API-Key: __YOUR_API_KEY_HERE__
Content-Type: application/json
{
  "text": "I need to send a package to my friend Amanda Millers house at 525 Winchester Blvd in San Jose (thats california, obviously). The zip code there is 95128.",
  "address": {
    "name": "Dr. Amanda L Miller",
    "country_code": "US"
  }
}

Entity Types

ShipEngine's address recognition can currently recognize the following entity types:

Entity Type	Recognized Attributes
address	direction: enumerated string ("from" or "to") name: string company_name: string phone: string address_line1: string address_line2: string address_line3: string city_locality: string state_province: string postal_code: string country_code: string address_residential_indicator: enumerated string ("yes", "no", or "unknown")
address_line	line: number(usually 1, 2 or 3) value: string (ex: "525 Winchester Blvd")
city_locality	value: string
country	name: string value: string
number	type: enumerated string ("cardial", "ordinal", "or "percentage") value: number
person	value: string
phone_number	value: string
postal_code	value: string
residential_indicator	value: enumerated string ("yes", "no", or "unknown")
state_province	name: string (ex: "Texas", "Quebec", "New South Wales") value: string (ex: "TX", "QC", "NSW") country: string (ex: "US", "CA", "AU")