The Python OCR SDK supports the Driver License API.

Using the sample below, we are going to illustrate how to extract the data that we want using the OCR SDK.
Driver License sample

Quick-Start

from mindee import Client, PredictResponse, product

# Init a new client
mindee_client = Client(api_key="my-api-key")

# Load a file from disk
input_doc = mindee_client.source_from_path("/path/to/the/file.ext")

# Load a file from disk and parse it.
# The endpoint name must be specified since it cannot be determined from the class.
result: PredictResponse = mindee_client.parse(product.us.DriverLicenseV1, input_doc)

# Print a summary of the API result
print(result.document)

# Print the document-level summary
# print(result.document.inference.prediction)

Output (RST):

########
Document
########
:Mindee ID: bf70068d-d3d6-49dc-b93a-b4b7d156fc3d
:Filename: default_sample.jpg

Inference
#########
:Product: mindee/us_driver_license v1.0
:Rotation applied: Yes

Prediction
==========
:State: AZ
:Driver License ID: D12345678
:Expiry Date: 2018-02-01
:Date Of Issue: 2013-01-10
:Last Name: SAMPLE
:First Name: JELANI
:Address: 123 MAIN STREET PHOENIX AZ 85007
:Date Of Birth: 1957-02-01
:Restrictions: NONE
:Endorsements: NONE
:Driver License Class: D
:Sex: M
:Height: 5-08
:Weight: 185
:Hair Color: BRO
:Eye Color: BRO
:Document Discriminator: 1234567890123456

Page Predictions
================

Page 0
------
:Photo: Polygon with 4 points.
:Signature: Polygon with 4 points.
:State: AZ
:Driver License ID: D12345678
:Expiry Date: 2018-02-01
:Date Of Issue: 2013-01-10
:Last Name: SAMPLE
:First Name: JELANI
:Address: 123 MAIN STREET PHOENIX AZ 85007
:Date Of Birth: 1957-02-01
:Restrictions: NONE
:Endorsements: NONE
:Driver License Class: D
:Sex: M
:Height: 5-08
:Weight: 185
:Hair Color: BRO
:Eye Color: BRO
:Document Discriminator: 1234567890123456

Field Types

Standard Fields

These fields are generic and used in several products.

BaseField

Each prediction object contains a set of fields that inherit from the generic BaseField class.
A typical BaseField object will have the following attributes:

value (Union[float, str]): corresponds to the field value. Can be None if no value was extracted.
confidence (float): the confidence score of the field prediction.
bounding_box ([Point, Point, Point, Point]): contains exactly 4 relative vertices (points) coordinates of a right rectangle containing the field in the document.
polygon (List[Point]): contains the relative vertices coordinates (Point) of a polygon containing the field in the image.
page_id (int): the ID of the page, always None when at document-level.
reconstructed (bool): indicates whether an object was reconstructed (not extracted as the API gave it).

Note: A Point simply refers to a List of two numbers ([float, float]).

Aside from the previous attributes, all basic fields have access to a custom __str__ method that can be used to print their value as a string.

DateField

Aside from the basic BaseField attributes, the date field DateField also implements the following:

date_object (Date): an accessible representation of the value as a python object. Can be None.

PositionField

The position field PositionField does not implement all the basic BaseField attributes, only bounding_box, polygon and page_id. On top of these, it has access to:

rectangle ([Point, Point, Point, Point]): a Polygon with four points that may be oriented (even beyond canvas).
quadrangle ([Point, Point, Point, Point]): a free polygon made up of four points.

StringField

The text field StringField only has one constraint: its value is an Optional[str].

Page-Level Fields

Some fields are constrained to the page level, and so will not be retrievable at document level.

Attributes

The following fields are extracted for Driver License V1:

Address

address (StringField): US driver license holders address

print(result.document.inference.prediction.address.value)

Date Of Birth

date_of_birth (DateField): US driver license holders date of birth

print(result.document.inference.prediction.date_of_birth.value)

Document Discriminator

dd_number (StringField): Document Discriminator Number of the US Driver License

print(result.document.inference.prediction.dd_number.value)

Driver License Class

dl_class (StringField): US driver license holders class

print(result.document.inference.prediction.dl_class.value)

Driver License ID

driver_license_id (StringField): ID number of the US Driver License.

print(result.document.inference.prediction.driver_license_id.value)

Endorsements

endorsements (StringField): US driver license holders endorsements

print(result.document.inference.prediction.endorsements.value)

Expiry Date

expiry_date (DateField): Date on which the documents expires.

print(result.document.inference.prediction.expiry_date.value)

Eye Color

eye_color (StringField): US driver license holders eye colour

print(result.document.inference.prediction.eye_color.value)

First Name

first_name (StringField): US driver license holders first name(s)

print(result.document.inference.prediction.first_name.value)

Hair Color

hair_color (StringField): US driver license holders hair colour

print(result.document.inference.prediction.hair_color.value)

Height

height (StringField): US driver license holders hight

print(result.document.inference.prediction.height.value)

Date Of Issue

issued_date (DateField): Date on which the documents was issued.

print(result.document.inference.prediction.issued_date.value)

Last Name

last_name (StringField): US driver license holders last name

print(result.document.inference.prediction.last_name.value)

Photo

📄photo (PositionField): Has a photo of the US driver license holder

for photo_elem in result.document.photo:
    print(photo_elem.polygon)

Restrictions

restrictions (StringField): US driver license holders restrictions

print(result.document.inference.prediction.restrictions.value)

Sex

sex (StringField): US driver license holders gender

print(result.document.inference.prediction.sex.value)

Signature

📄signature (PositionField): Has a signature of the US driver license holder

for signature_elem in result.document.signature:
    print(signature_elem.polygon)

State

state (StringField): US State

print(result.document.inference.prediction.state.value)

Weight

weight (StringField): US driver license holders weight

print(result.document.inference.prediction.weight.value)

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