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Resume OCR Java

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The Java 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

import com.mindee.MindeeClient;
import com.mindee.input.LocalInputSource;
import com.mindee.parsing.common.AsyncPredictResponse;
import com.mindee.product.resume.ResumeV1;
import java.io.File;
import java.io.IOException;

public class SimpleMindeeClient {

  public static void main(String[] args) throws IOException, InterruptedException {
    String apiKey = "my-api-key";
    String filePath = "/path/to/the/file.ext";

    // Init a new client
    MindeeClient mindeeClient = new MindeeClient(apiKey);

    // Load a file from disk
    LocalInputSource inputSource = new LocalInputSource(new File(filePath));

    // Parse the file asynchronously
    AsyncPredictResponse<ResumeV1> response = mindeeClient.enqueueAndParse(
        ResumeV1.class,
        inputSource
    );

    // Print a summary of the response
    System.out.println(response.toString());

    // Print a summary of the predictions
//  System.out.println(response.getDocumentObj().toString());

    // Print the document-level predictions
//    System.out.println(response.getDocumentObj().getInference().getPrediction().toString());

    // Print the page-level predictions
//    response.getDocumentObj().getInference().getPages().forEach(
//        page -> System.out.println(page.toString())
//    );
  }

}

Output (RST):

########
Document
########
:Mindee ID: 9daa3085-152c-454e-9245-636f13fc9dc3
:Filename: default_sample.jpg

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

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      | Beginner             |
  +----------+----------------------+
:Hard Skills: HTML5
              PHP OOP
              JavaScript
              CSS
              MySQL
              SQL
:Soft Skills: Project management
              Creative design
              Strong decision maker
              Innovative
              Complex problem solver
              Service-focused
:Education:
  +-----------------+---------------------------+-----------+----------+---------------------------+-------------+------------+
  | Domain          | Degree                    | End Month | End Year | School                    | Start Month | Start Year |
  +=================+===========================+===========+==========+===========================+=============+============+
  | Computer Inf... | Bachelor                  |           | 2014     | Columbia University, NY   |             |            |
  +-----------------+---------------------------+-----------+----------+---------------------------+-------------+------------+
:Professional Experiences:
  +-----------------+------------+--------------------------------------+---------------------------+-----------+----------+----------------------+-------------+------------+
  | Contract Type   | Department | Description                          | Employer                  | End Month | End Year | Role                 | Start Month | Start Year |
  +=================+============+======================================+===========================+===========+==========+======================+=============+============+
  |                 |            | Cooperate with designers to creat... | Luna Web Design, New York | 05        | 2019     | Web Developer        | 09          | 2015       |
  +-----------------+------------+--------------------------------------+---------------------------+-----------+----------+----------------------+-------------+------------+
:Certificates:
  +------------+--------------------------------+---------------------------+------+
  | Grade      | Name                           | Provider                  | Year |
  +============+================================+===========================+======+
  |            | PHP Framework (certificate)... |                           |      |
  +------------+--------------------------------+---------------------------+------+

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:

  • confidence (Double): the confidence score of the field prediction.
  • boundingBox (Polygon): contains exactly 4 relative vertices (points) coordinates of a right rectangle containing the field in the document.
  • polygon (Polygon): contains the relative vertices coordinates (polygon extends List<Point>) of a polygon containing the field in the image.
  • pageId (Integer): the ID of the page, always null when at document-level.

Note: A Point simply refers to a List of Double.

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

ClassificationField

The classification field ClassificationField extends BaseField, but also implements:

  • value (strong): corresponds to the field value.
  • confidence (double): the confidence score of the field prediction.

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

StringField

The text field StringField extends BaseField, but also implements:

  • value (String): corresponds to the field value.
  • rawValue (String): corresponds to the raw value as it appears on the document.

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 (String): The grade obtained for the certificate.
  • name (String): The name of certification.
  • provider (String): The organization or institution that issued the certificate.
  • year (String): 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:

  • degreeDomain (String): The area of study or specialization.
  • degreeType (String): The type of degree obtained, such as Bachelor's, Master's, or Doctorate.
  • endMonth (String): The month when the education program or course was completed.
  • endYear (String): The year when the education program or course was completed.
  • school (String): The name of the school.
  • startMonth (String): The month when the education program or course began.
  • startYear (String): 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 (String): The language's ISO 639 code.
  • level (String): The candidate's level for the language.

Possible values include:

  • Native
  • Fluent
  • Proficient
  • Intermediate
  • Beginner

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:

  • contractType (String): The type of contract for the professional experience.

Possible values include:

  • Full-Time
  • Part-Time
  • Internship
  • Freelance
  • department (String): The specific department or division within the company.
  • description (String): The description of the professional experience as written in the document.
  • employer (String): The name of the company or organization.
  • endMonth (String): The month when the professional experience ended.
  • endYear (String): The year when the professional experience ended.
  • role (String): The position or job title held by the candidate.
  • startMonth (String): The month when the professional experience began.
  • startYear (String): 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 (String): The name of the social network.
  • url (String): The URL of the social network.

Attributes

The following fields are extracted for Resume V1:

Address

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

System.out.println(result.getDocument().getInference().getPrediction().getAddress().value);

Certificates

certificates(List<ResumeV1Certificate>): The list of certificates obtained by the candidate.

for (certificatesElem : result.getDocument().getInference().getPrediction().getCertificates())
{
    System.out.println(certificatesElem.value);
}

Document Language

documentLanguage: The ISO 639 code of the language in which the document is written.

System.out.println(result.getDocument().getInference().getPrediction().getDocumentLanguage().value);

Document Type

documentType: The type of the document sent.

Possible values include:

  • RESUME
  • MOTIVATION_LETTER
  • RECOMMENDATION_LETTER
System.out.println(result.getDocument().getInference().getPrediction().getDocumentType().value);

Education

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

for (educationElem : result.getDocument().getInference().getPrediction().getEducation())
{
    System.out.println(educationElem.value);
}

Email Address

emailAddress: The email address of the candidate.

System.out.println(result.getDocument().getInference().getPrediction().getEmailAddress().value);

Given Names

givenNames: The candidate's first or given names.

for (givenNamesElem : result.getDocument().getInference().getPrediction().getGivenNames())
{
    System.out.println(givenNamesElem.value);
}

Hard Skills

hardSkills: The list of the candidate's technical abilities and knowledge.

for (hardSkillsElem : result.getDocument().getInference().getPrediction().getHardSkills())
{
    System.out.println(hardSkillsElem.value);
}

Job Applied

jobApplied: The position that the candidate is applying for.

System.out.println(result.getDocument().getInference().getPrediction().getJobApplied().value);

Languages

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

for (languagesElem : result.getDocument().getInference().getPrediction().getLanguages())
{
    System.out.println(languagesElem.value);
}

Nationality

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

System.out.println(result.getDocument().getInference().getPrediction().getNationality().value);

Phone Number

phoneNumber: The phone number of the candidate.

System.out.println(result.getDocument().getInference().getPrediction().getPhoneNumber().value);

Profession

profession: The candidate's current profession.

System.out.println(result.getDocument().getInference().getPrediction().getProfession().value);

Professional Experiences

professionalExperiences(List<ResumeV1ProfessionalExperience>): The list of the candidate's professional experiences.

for (professionalExperiencesElem : result.getDocument().getInference().getPrediction().getProfessionalExperiences())
{
    System.out.println(professionalExperiencesElem.value);
}

Social Networks

socialNetworksUrls(List<ResumeV1SocialNetworksUrl>): The list of social network profiles of the candidate.

for (socialNetworksUrlsElem : result.getDocument().getInference().getPrediction().getSocialNetworksUrls())
{
    System.out.println(socialNetworksUrlsElem.value);
}

Soft Skills

softSkills: The list of the candidate's interpersonal and communication abilities.

for (softSkillsElem : result.getDocument().getInference().getPrediction().getSoftSkills())
{
    System.out.println(softSkillsElem.value);
}

Surnames

surnames: The candidate's last names.

for (surnamesElem : result.getDocument().getInference().getPrediction().getSurnames())
{
    System.out.println(surnamesElem.value);
}

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Updated 8 days ago