Update a Business Card

PUT 
/api/v8/partner/business-cards/:document_id

Veryfi's Update a Document by ID endpoint allows you to change a Document that Veryfi's Machine Learning models have already processed. This feature will enable users to update Documents previously processed by Veryfi to ensure data extraction accuracy. Updating a Document is especially useful for correcting mistakes and updating information over time. By changing a processed Document, Veryfi's Machine Learning models can re-learn the updated information, allowing them to stay accurate and up-to-date.

Request

Path Parameters

document_id int64required

The unique identifier of the document.

application/json

Body

external_id stringdeprecated

Possible values: non-empty

Deprecated 2025-01-09, use meta.external_id instead.

meta object

external_id string

Possible values: non-empty

tags string[]

Possible values: non-empty

Default value: ``

Responses

200

A processed Business Card response.

application/json

Schema

Schema

external_id string

Possible values: non-empty

A custom identification value. Use this if you would like to assign your own ID to documents. This parameter is useful when mapping this document to a service or resource outside Veryfi.

meta object

external_id string

Possible values: non-empty

A custom identification value. Use this if you would like to assign your own ID to documents. This parameter is useful when mapping this document to a service or resource outside Veryfi.

pages object[]

Array [

ocr_score number

Possible values: <= 1

The average OCR score of the page.

width integer

The width of the page.

height integer

The height of the page.

]

tags string[]

Possible values: non-empty

Default value: ``

Tags associated with the document.

ocr_score number

Possible values: <= 1

The average OCR score of the whole document.

model string

Possible values: non-empty

The version of the model used to process the document.

text string

The text returned from converting the document into a machine-readable text format.

id integerrequired

The unique number created to identify the document.

created_date date-timerequired
updated_date date-timerequired
organization objectrequired

The name of the business found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

logo_url objectrequired

A signed URL to the logo for the organization found on the document. The URL expires 15 minutes after the Document Response is returned and is re-assigned on every GET request.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

img_url object required

anyOf

MOD1

string

MOD2

string

person objectrequired

The full name of the individual found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

parsed_name objectrequired

An object that represents the person found on the document.

family_name object

The last name or found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

given_name object

The first name found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

middle_name object

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

suffix object

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

prefix object

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

title objectrequired

The job title of the person found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

email objectrequired

The email address found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

address objectrequired

The address found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

parsed_address objectrequired

An object representing detailed address-related information on the document, such as the city, state, street address, unit number, etc.

building object

The building name e.g. 'Project 8' or 'Empire State Building'

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

city object

The settlement including cities, towns, villages, hamlets, localities, etc.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

country object

The sovereign nations and their dependent territories, anything with an ISO-3166 code.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

country_alpha_2 object

The detected ISO 3166-1 alpha-2 code for a given country.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

postcode object

The postal code used for mail sorting

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

state object

A first-level administrative division. Scotland, Northern Ireland, Wales, and England in the UK are mapped to "state" as well

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

street_address object

A sum of house_number road, building, unit.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

house object

The venue name e.g. "Brooklyn Academy of Music", and building names e.g. "Empire State Building".

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

house_number object

Usually refers to the external (street-facing) building number. In some countries this may be a compound, hyphenated number, which also includes an apartment number, or a block number (a la Japan).

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

road object

Street name(s)

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

unit object

An apartment, unit, office, lot, or other secondary unit designator.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

level object

The expressions indicating a floor number e.g. "3rd Floor", "Ground Floor", etc.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

staircase object

Numbered/lettered staircase

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

entrance object

Numbered/lettered entrance

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

po_box object

The post office box, typically found in non-physical (mail-only) addresses.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

suburb object

An unofficial neighborhood name like "Harlem", "South Bronx", or "Crown Heights".

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

city_district object

The boroughs or districts within a city that serve some official purpose e.g. "Brooklyn" or "Hackney" or "Bratislava IV".

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

island object

Named islands e.g. "Maui"

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

state_district object

Usually a second-level administrative division or county.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

country_region object

Informal subdivision of a country without any political status.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

world_region object

Only used for appending "West Indies" after the country name, a pattern frequently used in the English-speaking Caribbean e.g. "Jamaica, West Indies".

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

value stringrequired

Possible values: non-empty

The extracted value.

mobile objectrequired

The mobile number found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

phone objectrequired

The phone number found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

fax objectrequired

The fax number found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

web objectrequired

The web page found on the document.

score number

Possible values: <= 1

The score shows how confident the model is that the predicted value belongs to the field. See confidence scores explained for more information.

bounding_region number[]

Possible values: >= 8, <= 8

An array containing (x,y) coordinates in the format [x1,y1,x2,y2,x3,y3,x4,y4]` for skewed images and handwritten fields. The bounding region is more precise than bounding box, otherwise it's the same.

bounding_box object[]

Possible values: >= 5, <= 5

An array containing relative coordinates in the format [page_number,x1,y1,x2,y2] for the extracted field from img_url before any rotation.

Array [

anyOf

MOD1

number

MOD2

integer

]

ocr_score number

Possible values: <= 1

The score which shows how confident the model in recognizing value symbols. See confidence scores explained for more information.

rotation integer

Possible values: [0, 90, 180, 270]

The angle of rotation of the document in degrees.

value stringrequired

Possible values: non-empty

The extracted value.

Example (from schema)

{
  "external_id": "string",
  "meta": {
    "external_id": "string",
    "pages": [
      {
        "ocr_score": 0,
        "width": 0,
        "height": 0
      }
    ],
    "tags": [
      "string"
    ],
    "ocr_score": 0,
    "model": "string"
  },
  "text": "string",
  "id": 0,
  "created_date": "2025-08-03T02:06:54.702Z",
  "updated_date": "2025-08-03T02:06:54.702Z",
  "organization": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "logo_url": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "img_url": "string",
  "person": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "parsed_name": {
    "family_name": {
      "score": 0,
      "value": "string"
    },
    "given_name": {
      "score": 0,
      "value": "string"
    },
    "middle_name": {
      "score": 0,
      "value": "string"
    },
    "suffix": {
      "score": 0,
      "value": "string"
    },
    "prefix": {
      "score": 0,
      "value": "string"
    }
  },
  "title": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "email": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "address": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "parsed_address": {
    "building": {
      "score": 0,
      "value": "string"
    },
    "city": {
      "score": 0,
      "value": "string"
    },
    "country": {
      "score": 0,
      "value": "string"
    },
    "country_alpha_2": {
      "score": 0,
      "value": "string"
    },
    "postcode": {
      "score": 0,
      "value": "string"
    },
    "state": {
      "score": 0,
      "value": "string"
    },
    "street_address": {
      "score": 0,
      "value": "string"
    },
    "house": {
      "score": 0,
      "value": "string"
    },
    "house_number": {
      "score": 0,
      "value": "string"
    },
    "road": {
      "score": 0,
      "value": "string"
    },
    "unit": {
      "score": 0,
      "value": "string"
    },
    "level": {
      "score": 0,
      "value": "string"
    },
    "staircase": {
      "score": 0,
      "value": "string"
    },
    "entrance": {
      "score": 0,
      "value": "string"
    },
    "po_box": {
      "score": 0,
      "value": "string"
    },
    "suburb": {
      "score": 0,
      "value": "string"
    },
    "city_district": {
      "score": 0,
      "value": "string"
    },
    "island": {
      "score": 0,
      "value": "string"
    },
    "state_district": {
      "score": 0,
      "value": "string"
    },
    "country_region": {
      "score": 0,
      "value": "string"
    },
    "world_region": {
      "score": 0,
      "value": "string"
    }
  },
  "mobile": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "phone": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "fax": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  },
  "web": {
    "score": 0,
    "bounding_region": [
      0
    ],
    "bounding_box": [
      0,
      0
    ],
    "ocr_score": 0,
    "rotation": 0,
    "value": "string"
  }
}

400

User error

application/json

Schema

Schema

oneOf

MALFORMED_PARAMETERS

status string

Default value: fail

error string

Default value: Malformed parameters

Example (from schema)

{}

403

User error

application/json

Schema

Schema

oneOf

AUTHENTICATION_CREDENTIALS_WERE_NOT_PROVIDED

status string

Default value: fail

error string

Default value: Authentication credentials were not provided.

Example (from schema)

{}

404

Not found

application/json

Schema

Schema

oneOf

NOT_FOUND

status string

Default value: fail

error string

Default value: Not found.

DOCUMENT_NOT_FOUND

status string

Default value: fail

error string

Default value: Document Not Found

Example (from schema)

{}

413

Request body too large

application/json

Schema

Schema

oneOf

REQUEST_BODY_TOO_LARGE

status string

Default value: fail

error string

Default value: Request body too large

Example (from schema)

{}

429

Rate limit

application/json

Schema

Schema

oneOf

YOU_HAVE_BEEN_RATE_LIMITED

status string

Default value: fail

error string

Default value: You have been rate limited

details undefined[]

Default value: [object Object]

Example (from schema)

{}

499

User error

application/json

Schema

Schema

oneOf

CLIENT_CLOSED_REQUEST_OR_LOST_CONNECTION

status string

Default value: fail

error string

Default value: Client closed request or lost connection

Example (from schema)

{}

500

Unexpected error

503

Service is temporarily unavailable

application/json

Schema

Schema

oneOf

SERVICE_IS_TEMPORARILY_UNAVAILABLE_PLEASE_TRY_AGAIN_LATER

status string

Default value: fail

error string

Default value: Service is temporarily unavailable. Please try again later

Example (from schema)

{}

504

Gateway timeout. Returned if request takes more than 100 seconds. The request might finish successfully later.

application/json

Schema

Schema

oneOf

GATEWAY_TIMEOUT

status string

Default value: fail

error string

Default value: Gateway timeout

Example (from schema)

{}

default

General error response

application/json

Schema

Schema

status string

Default value: fail

error stringrequired
details object[]

Example (from schema)

{
  "status": "fail",
  "error": "string",
  "details": [
    {}
  ]
}

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