The Python OCR SDK supports the Carte Grise API.

Using the sample below, we are going to illustrate how to extract the data that we want using the OCR SDK.
Carte Grise 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.fr.CarteGriseV1, 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: 4443182b-57c1-4426-a288-01b94f226e84
:Filename: default_sample.jpg

Inference
#########
:Product: mindee/carte_grise v1.1
:Rotation applied: Yes

Prediction
==========
:a: AB-123-CD
:b: 1998-01-05
:c1: DUPONT YVES
:c3: 27 RUE DES ROITELETS 59169 FERIN LES BAINS FRANCE
:c41: 2 DELAROCHE
:c4a: EST LE PROPRIETAIRE DU VEHICULE
:d1:
:d3: MODELE
:e: VFS1V2009AS1V2009
:f1: 1915
:f2: 1915
:f3: 1915
:g: 3030
:g1: 1307
:i: 2009-12-04
:j: N1
:j1: VP
:j2: AA
:j3: CI
:p1: 1900
:p2: 90
:p3: GO
:p6: 6
:q: 006
:s1: 5
:s2:
:u1: 77
:u2: 3000
:v7: 155
:x1: 2011-07-06
:y1: 17835
:y2:
:y3: 0
:y4: 4
:y5: 2.5
:y6: 178.35
:Formula Number: 2009AS05284
:Owner's First Name: YVES
:Owner's Surname: DUPONT
:MRZ Line 1:
:MRZ Line 2: CI<<MARQUES<<<<<<<MODELE<<<<<<<2009AS0528402

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

Page 0
------
:a: AB-123-CD
:b: 1998-01-05
:c1: DUPONT YVES
:c3: 27 RUE DES ROITELETS 59169 FERIN LES BAINS FRANCE
:c41: 2 DELAROCHE
:c4a: EST LE PROPRIETAIRE DU VEHICULE
:d1:
:d3: MODELE
:e: VFS1V2009AS1V2009
:f1: 1915
:f2: 1915
:f3: 1915
:g: 3030
:g1: 1307
:i: 2009-12-04
:j: N1
:j1: VP
:j2: AA
:j3: CI
:p1: 1900
:p2: 90
:p3: GO
:p6: 6
:q: 006
:s1: 5
:s2:
:u1: 77
:u2: 3000
:v7: 155
:x1: 2011-07-06
:y1: 17835
:y2:
:y3: 0
:y4: 4
:y5: 2.5
:y6: 178.35
:Formula Number: 2009AS05284
:Owner's First Name: YVES
:Owner's Surname: DUPONT
:MRZ Line 1:
:MRZ Line 2: CI<<MARQUES<<<<<<<MODELE<<<<<<<2009AS0528402

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.

StringField

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

Attributes

The following fields are extracted for Carte Grise V1:

a

a (StringField): The vehicle's license plate number.

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

b

b (DateField): The vehicle's first release date.

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

c1

c1 (StringField): The vehicle owner's full name including maiden name.

print(result.document.inference.prediction.c1.value)

c3

c3 (StringField): The vehicle owner's address.

print(result.document.inference.prediction.c3.value)

c41

c41 (StringField): Number of owners of the license certificate.

print(result.document.inference.prediction.c41.value)

c4a

c4a (StringField): Mentions about the ownership of the vehicle.

print(result.document.inference.prediction.c4a.value)

d1

d1 (StringField): The vehicle's brand.

print(result.document.inference.prediction.d1.value)

d3

d3 (StringField): The vehicle's commercial name.

print(result.document.inference.prediction.d3.value)

e

e (StringField): The Vehicle Identification Number (VIN).

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

f1

f1 (StringField): The vehicle's maximum admissible weight.

print(result.document.inference.prediction.f1.value)

f2

f2 (StringField): The vehicle's maximum admissible weight within the license's state.

print(result.document.inference.prediction.f2.value)

f3

f3 (StringField): The vehicle's maximum authorized weight with coupling.

print(result.document.inference.prediction.f3.value)

Formula Number

formula_number (StringField): The document's formula number.

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

g

g (StringField): The vehicle's weight with coupling if tractor different than category M1.

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

g1

g1 (StringField): The vehicle's national empty weight.

print(result.document.inference.prediction.g1.value)

i

i (DateField): The car registration date of the given certificate.

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

j

j (StringField): The vehicle's category.

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

j1

j1 (StringField): The vehicle's national type.

print(result.document.inference.prediction.j1.value)

j2

j2 (StringField): The vehicle's body type (CE).

print(result.document.inference.prediction.j2.value)

j3

j3 (StringField): The vehicle's body type (National designation).

print(result.document.inference.prediction.j3.value)

MRZ Line 1

mrz1 (StringField): Machine Readable Zone, first line.

print(result.document.inference.prediction.mrz1.value)

MRZ Line 2

mrz2 (StringField): Machine Readable Zone, second line.

print(result.document.inference.prediction.mrz2.value)

Owner's First Name

owner_first_name (StringField): The vehicle's owner first name.

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

Owner's Surname

owner_surname (StringField): The vehicle's owner surname.

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

p1

p1 (StringField): The vehicle engine's displacement (cm3).

print(result.document.inference.prediction.p1.value)

p2

p2 (StringField): The vehicle's maximum net power (kW).

print(result.document.inference.prediction.p2.value)

p3

p3 (StringField): The vehicle's fuel type or energy source.

print(result.document.inference.prediction.p3.value)

p6

p6 (StringField): The vehicle's administrative power (fiscal horsepower).

print(result.document.inference.prediction.p6.value)

q

q (StringField): The vehicle's power to weight ratio.

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

s1

s1 (StringField): The vehicle's number of seats.

print(result.document.inference.prediction.s1.value)

s2

s2 (StringField): The vehicle's number of standing rooms (person).

print(result.document.inference.prediction.s2.value)

u1

u1 (StringField): The vehicle's sound level (dB).

print(result.document.inference.prediction.u1.value)

u2

u2 (StringField): The vehicle engine's rotation speed (RPM).

print(result.document.inference.prediction.u2.value)

v7

v7 (StringField): The vehicle's CO2 emission (g/km).

print(result.document.inference.prediction.v7.value)

x1

x1 (StringField): Next technical control date.

print(result.document.inference.prediction.x1.value)

y1

y1 (StringField): Amount of the regional proportional tax of the registration (in euros).

print(result.document.inference.prediction.y1.value)

y2

y2 (StringField): Amount of the additional parafiscal tax of the registration (in euros).

print(result.document.inference.prediction.y2.value)

y3

y3 (StringField): Amount of the additional CO2 tax of the registration (in euros).

print(result.document.inference.prediction.y3.value)

y4

y4 (StringField): Amount of the fee for managing the registration (in euros).

print(result.document.inference.prediction.y4.value)

y5

y5 (StringField): Amount of the fee for delivery of the registration certificate in euros.

print(result.document.inference.prediction.y5.value)

y6

y6 (StringField): Total amount of registration fee to be paid in euros.

print(result.document.inference.prediction.y6.value)

Questions?

Join our Slack