The Python OCR SDK supports the Resume API.

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

Quick-Start

from mindee import Client, product, AsyncPredictResponse
from time import sleep

# 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 enqueue it.
result: AsyncPredictResponse = mindee_client.enqueue_and_parse(
    product.ResumeV1,
    input_doc,
)

# Print a brief summary of the parsed data
print(result.document)

Output (RST):

########
Document
########
:Mindee ID: bc80bae0-af75-4464-95a9-2419403c75bf
:Filename: default_sample.jpg

Inference
#########
:Product: mindee/resume v1.0
:Rotation applied: No

Prediction
==========
:Document Language: ENG
:Document Type: RESUME
:Given Names: Christopher
:Surnames: Morgan
:Nationality:
:Email Address: [email protected]
:Phone Number: +44 (0) 20 7666 8555
:Address: 177 Great Portland Street, London W5W 6PQ
:Social Networks:
  +----------------------+----------------------------------------------------+
  | Name                 | URL                                                |
  +======================+====================================================+
  | LinkedIn             | linkedin.com/christopher.morgan                    |
  +----------------------+----------------------------------------------------+
:Profession: Senior Web Developer
:Job Applied:
:Languages:
  +----------+----------------------+
  | Language | Level                |
  +==========+======================+
  | SPA      | Fluent               |
  +----------+----------------------+
  | ZHO      | Beginner             |
  +----------+----------------------+
  | DEU      | Intermediate         |
  +----------+----------------------+
:Hard Skills: HTML5
              PHP OOP
              JavaScript
              CSS
              MySQL
:Soft Skills: Project management
              Strong decision maker
              Innovative
              Complex problem solver
              Creative design
              Service-focused
:Education:
  +-----------------+---------------------------+-----------+----------+---------------------------+-------------+------------+
  | Domain          | Degree                    | End Month | End Year | School                    | Start Month | Start Year |
  +=================+===========================+===========+==========+===========================+=============+============+
  | Computer Inf... | Bachelor                  |           |          | Columbia University, NY   |             | 2014       |
  +-----------------+---------------------------+-----------+----------+---------------------------+-------------+------------+
:Professional Experiences:
  +-----------------+------------+---------------------------+-----------+----------+----------------------+-------------+------------+
  | Contract Type   | Department | Employer                  | End Month | End Year | Role                 | Start Month | Start Year |
  +=================+============+===========================+===========+==========+======================+=============+============+
  | Full-Time       |            | Luna Web Design, New York | 05        | 2019     | Web Developer        | 09          | 2015       |
  +-----------------+------------+---------------------------+-----------+----------+----------------------+-------------+------------+
:Certificates:
  +------------+--------------------------------+---------------------------+------+
  | Grade      | Name                           | Provider                  | Year |
  +============+================================+===========================+======+
  |            | PHP Framework (certificate)... |                           | 2014 |
  +------------+--------------------------------+---------------------------+------+
  |            | Programming Languages: Java... |                           |      |
  +------------+--------------------------------+---------------------------+------+

Field Types

Standard Fields

These fields are generic and used in several products.

BasicField

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, is 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.

ClassificationField

The classification field ClassificationField does not implement all the basic BaseField attributes. It only implements value, confidence and page_id.

Note: a classification field's value is always a str`.

StringField

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

Specific Fields

Fields which are specific to this product; they are not used in any other product.

Certificates Field

The list of certificates obtained by the candidate.

A ResumeV1Certificate implements the following attributes:

grade (str): The grade obtained for the certificate.
name (str): The name of certification.
provider (str): The organization or institution that issued the certificate.
year (str): The year when a certificate was issued or received.
Fields which are specific to this product; they are not used in any other product.

Education Field

The list of the candidate's educational background.

A ResumeV1Education implements the following attributes:

degree_domain (str): The area of study or specialization.
degree_type (str): The type of degree obtained, such as Bachelor's, Master's, or Doctorate.
end_month (str): The month when the education program or course was completed.
end_year (str): The year when the education program or course was completed.
school (str): The name of the school.
start_month (str): The month when the education program or course began.
start_year (str): The year when the education program or course began.
Fields which are specific to this product; they are not used in any other product.

Languages Field

The list of languages that the candidate is proficient in.

A ResumeV1Language implements the following attributes:

language (str): The language's ISO 639 code.
level (str): The candidate's level for the language.
Fields which are specific to this product; they are not used in any other product.

Professional Experiences Field

The list of the candidate's professional experiences.

A ResumeV1ProfessionalExperience implements the following attributes:

contract_type (str): The type of contract for the professional experience.
department (str): The specific department or division within the company.
employer (str): The name of the company or organization.
end_month (str): The month when the professional experience ended.
end_year (str): The year when the professional experience ended.
role (str): The position or job title held by the candidate.
start_month (str): The month when the professional experience began.
start_year (str): The year when the professional experience began.
Fields which are specific to this product; they are not used in any other product.

Social Networks Field

The list of social network profiles of the candidate.

A ResumeV1SocialNetworksUrl implements the following attributes:

name (str): The name of the social network.
url (str): The URL of the social network.

Attributes

The following fields are extracted for Resume V1:

Address

address (StringField): The location information of the candidate, including city, state, and country.

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

Certificates

certificates (List[ResumeV1Certificate]): The list of certificates obtained by the candidate.

for certificates_elem in result.document.inference.prediction.certificates:
    print(certificates_elem.value)

Document Language

document_language (StringField): The ISO 639 code of the language in which the document is written.

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

Document Type

document_type (ClassificationField): The type of the document sent.

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

Education

education (List[ResumeV1Education]): The list of the candidate's educational background.

for education_elem in result.document.inference.prediction.education:
    print(education_elem.value)

Email Address

email_address (StringField): The email address of the candidate.

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

Given Names

given_names (List[StringField]): The candidate's first or given names.

for given_names_elem in result.document.inference.prediction.given_names:
    print(given_names_elem.value)

Hard Skills

hard_skills (List[StringField]): The list of the candidate's technical abilities and knowledge.

for hard_skills_elem in result.document.inference.prediction.hard_skills:
    print(hard_skills_elem.value)

Job Applied

job_applied (StringField): The position that the candidate is applying for.

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

Languages

languages (List[ResumeV1Language]): The list of languages that the candidate is proficient in.

for languages_elem in result.document.inference.prediction.languages:
    print(languages_elem.value)

Nationality

nationality (StringField): The ISO 3166 code for the country of citizenship of the candidate.

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

Phone Number

phone_number (StringField): The phone number of the candidate.

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

Profession

profession (StringField): The candidate's current profession.

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

Professional Experiences

professional_experiences (List[ResumeV1ProfessionalExperience]): The list of the candidate's professional experiences.

for professional_experiences_elem in result.document.inference.prediction.professional_experiences:
    print(professional_experiences_elem.value)

Social Networks

social_networks_urls (List[ResumeV1SocialNetworksUrl]): The list of social network profiles of the candidate.

for social_networks_urls_elem in result.document.inference.prediction.social_networks_urls:
    print(social_networks_urls_elem.value)

Soft Skills

soft_skills (List[StringField]): The list of the candidate's interpersonal and communication abilities.

for soft_skills_elem in result.document.inference.prediction.soft_skills:
    print(soft_skills_elem.value)

Surnames

surnames (List[StringField]): The candidate's last names.

for surnames_elem in result.document.inference.prediction.surnames:
    print(surnames_elem.value)

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