Resume OCR

Mindee’s Resume API uses deep learning to automatically, accurately, and instantaneously parse your documents details. In a few seconds, the API extracts a set of data from your PDFs or photos of resume, motivation and recommendation letters, including:

Document Language
Document Type
Given Names
Surnames
Nationality
Email Address
Phone Number
Address
Social Networks
Profession
Job Applied
Languages
Hard Skills
Soft Skills
Education
Professional Experiences
Certificates

Set up the API

Create an API key

To begin using the Mindee V1 OCR API, your first step is to create your V1 API key.

You'll need a Resume. You can use one of the sample documents provided below.

Access your Resume API by clicking on the corresponding product card in the Document Catalog

From the left navigation, go to documentation > API Reference, you'll find sample code in popular languages and command line.

from mindee import Client, product, AsyncPredictResponse

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

# 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)

const mindee = require("mindee");
// for TS or modules:
// import * as mindee from "mindee";

// Init a new client
const mindeeClient = new mindee.Client({ apiKey: "my-api-key-here" });

// Load a file from disk
const inputSource = mindeeClient.docFromPath("/path/to/the/file.ext");

// Parse the file
const apiResponse = mindeeClient.enqueueAndParse(
  mindee.product.ResumeV1,
  inputSource
);

// Handle the response Promise
apiResponse.then((resp) => {
  // print a string summary
  console.log(resp.document.toString());
});

using Mindee;
using Mindee.Input;
using Mindee.Product.Resume;

string apiKey = "my-api-key-here";
string filePath = "/path/to/the/file.ext";

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

// Load an input source as a path string
// Other input types can be used, as mentioned in the docs
var inputSource = new LocalInputSource(filePath);

// Call the product asynchronously with auto-polling
var response = await mindeeClient
    .EnqueueAndParseAsync<ResumeV1>(inputSource);

// Print a summary of all the predictions
System.Console.WriteLine(response.Document.ToString());

// Print only the document-level predictions
// System.Console.WriteLine(response.Document.Inference.Prediction.ToString());

#
# Install the Ruby client library by running:
# gem install mindee
#

require 'mindee'

# Init a new client
mindee_client = Mindee::Client.new(api_key: 'my-api-key')

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

# Parse the file
result = mindee_client.parse(
  input_source,
  Mindee::Product::Resume::ResumeV1
)

# Print a full summary of the parsed data in RST format
puts result.document

# Print the document-level parsed data
# puts result.document.inference.prediction

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-here";
    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())
//    );
  }

}

API_KEY='my-api-key-here'
ACCOUNT='mindee'
ENDPOINT='resume'
VERSION='1'
FILE_PATH='/path/to/your/file.png'

# Maximum amount of retries to get the result of a queue
MAX_RETRIES=10

# Delay between requests
DELAY=6

# Enqueue the document for async parsing
QUEUE_RESULT=$(curl -sS --request POST \
  -H "Authorization: Token $API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "document=@$FILE_PATH" \
  "https://api.mindee.net/v1/products/$ACCOUNT/$ENDPOINT/v$VERSION/predict_async")

# Status code sent back from the server
STATUS_CODE=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']status_code[\"|']:[\s][\"|']*[a-zA-Z0-9-]*" | rev | cut --complement -f2- -d" " | rev)

# Check that the document was properly queued
if [ -z "$STATUS_CODE" ] || [ "$STATUS_CODE" -gt 399 ] || [ "$STATUS_CODE" -lt 200 ]
then
  if [ -z "$STATUS_CODE" ]
  then
    echo "Request couldn't be processed."
    exit 1
  fi
  echo "Error $STATUS_CODE was returned by API during enqueuing. "

  # Print the additional details, if there are any:
  ERROR=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']error[\"|']:[\s]\{[^\}]*" | rev | cut --complement -f2- -d"{" | rev)
  if [ -z "$ERROR" ]
  then
    exit 1
  fi

  # Details on the potential error:
  ERROR_CODE=$(echo "$ERROR" | grep -oP "[\"|']code[\"|']:[\s]\"[^(\"|\')]*" | rev | cut --complement -f2- -d"\"" | rev)
  MESSAGE=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']message[\"|']:[\s]\"[^(\"|\')]*" | rev | cut --complement -f2- -d"\"" | rev)
  DETAILS=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']details[\"|']:[\s]\"[^(\"|\')]*" | rev | cut --complement -f2- -d"\"" | rev)
  echo "This was the given explanation:"
  echo "-------------------------"
  echo "Error Code: $ERROR_CODE"
  echo "Message: $MESSAGE"
  echo "Details: $DETAILS"
  echo "-------------------------"
  exit 1
else

  echo "File sent, starting to retrieve from server..."

  # Get the document's queue ID
  QUEUE_ID=$(echo "$QUEUE_RESULT" | grep -oP "[\"|']id[\"|']:[\s][\"|'][a-zA-Z0-9-]*" | rev | cut --complement -f2- -d"\"" | rev)

  # Amount of attempts to retrieve the parsed document were made
  TIMES_TRIED=1

  # Try to fetch the file until we get it, or until we hit the maximum amount of retries
  while [ "$TIMES_TRIED" -lt "$MAX_RETRIES" ]
  do
    # Wait for a bit at each step
    sleep $DELAY

    # Note: we use -L here because the location of the file might be behind a redirection
    PARSED_RESULT=$(curl -sS -L \
      -H "Authorization: Token $API_KEY" \
      "https://api.mindee.net/v1/products/$ACCOUNT/$ENDPOINT/v$VERSION/documents/queue/$QUEUE_ID")

    # Isolating the job (queue) & the status to monitor the document
    JOB=$(echo "$PARSED_RESULT" | grep -ioP "[\"|']job[\"|']:[\s]\{[^\}]*" | rev | cut --complement -f2- -d"{" | rev)
    QUEUE_STATUS=$(echo "$JOB" | grep -ioP "[\"|']status[\"|']:[\s][\"|'][a-zA-Z0-9-]*" | rev | cut --complement -f2- -d"\"" | rev)
    if [ "$QUEUE_STATUS" = "completed" ]
    then
      # Print the result
      echo "$PARSED_RESULT"

      # Optional: isolate the document:
      # DOCUMENT=$(echo "$PARSED_RESULT" | grep -ioP "[\"|']document[\"|']:[\s].*([\"|']job[\"|'])" | rev | cut -f2- -d"," | rev)
      # echo "{$DOCUMENT}"

      # Remark: on compatible shells, fields can also be extracted through the use of tools like jq:
      # DOCUMENT=$(echo "$PARSED_RESULT" | jq '.["document"]')
      exit 0
    fi
    TIMES_TRIED=$((TIMES_TRIED+1))
  done
fi

echo "Operation aborted, document not retrieved after $TIMES_TRIED tries"
exit 1

<?php

use Mindee\Client;
use Mindee\Product\Resume\ResumeV1;

// Init a new client
$mindeeClient = new Client("my-api-key-here");

// Load a file from disk
$inputSource = $mindeeClient->sourceFromPath("/path/to/the/file.ext");

// Parse the file asynchronously
$apiResponse = $mindeeClient->enqueueAndParse(ResumeV1::class, $inputSource);

echo $apiResponse->document;

Replace my-api-key-here with your new API key, or use the select an API key feature and it will be filled automatically.
Copy and paste the sample code of your desired choice in your application, code environment, terminal etc.
Replace /path/to/the/file.ext with the path to your input document.

Remember to replace with your V1 API key.

Run your code. You will receive a JSON response with the document details.

API Response

Here is the full JSON response you get when you call the API:

{
    "api_request": {
        "error": {},
        "resources": [
            "document",
            "job"
        ],
        "status": "success",
        "status_code": 200,
        "url": "https://api.mindee.net/v1/products/mindee/resume/v1/documents/a6601da5-d5cb-48c4-8d1a-de54d19b1bb5"
    },
    "document": {
        "id": "a6601da5-d5cb-48c4-8d1a-de54d19b1bb5",
        "inference": {
            "extras": {},
            "finished_at": "2024-10-29T10:21:14.778000",
            "is_rotation_applied": true,
            "pages": [
                {
                    "extras": {},
                    "id": 0,
                    "orientation": {
                        "value": 0
                    },
                    "prediction": {}
                }
            ],
            "prediction": {
                "address": {
                    "value": "177 Great Portland Street, London, W5W 6PQ"
                },
                "certificates": [
                    {
                        "grade": null,
                        "name": "PHP Framework (certificate):Zend,Codeigniter,Symfony",
                        "provider": null,
                        "year": null
                    },
                    {
                        "grade": null,
                        "name": "Programming Languages:JavaScript,HTML5, PHP OOP,CSS,SQL,MySQL",
                        "provider": null,
                        "year": null
                    }
                ],
                "document_language": {
                    "value": "ENG"
                },
                "document_type": {
                    "value": "RESUME"
                },
                "education": [
                    {
                        "degree_domain": "Computer Information Systems",
                        "degree_type": "Bachelor of Science",
                        "end_month": null,
                        "end_year": null,
                        "school": "Columbia University, NY",
                        "start_month": null,
                        "start_year": "2014"
                    }
                ],
                "email_address": {
                    "value": "[email protected]"
                },
                "given_names": [
                    {
                        "value": "Christopher"
                    }
                ],
                "hard_skills": [
                    {
                        "value": "HTML5"
                    },
                    {
                        "value": "PHP OOP"
                    },
                    {
                        "value": "JavaScript"
                    },
                    {
                        "value": "CSS"
                    },
                    {
                        "value": "MySQL"
                    }
                ],
                "job_applied": {
                    "value": null
                },
                "languages": [
                    {
                        "language": "SPA",
                        "level": "Fluent"
                    },
                    {
                        "language": "CHI",
                        "level": "Beginner"
                    },
                    {
                        "language": "GER",
                        "level": "Intermediate"
                    }
                ],
                "nationality": {
                    "value": null
                },
                "phone_number": {
                    "value": "+44 (0)20 7666 8555"
                },
                "profession": {
                    "value": "Senior Web Developer"
                },
                "professional_experiences": [
                    {
                        "contract_type": null,
                        "department": null,
                        "employer": "Luna Web Design, New York",
                        "end_month": "05",
                        "end_year": "2019",
                        "role": "Web Developer",
                        "start_month": "09",
                        "start_year": "2015"
                    }
                ],
                "social_networks_urls": [
                    {
                        "name": "LinkedIn",
                        "url": "linkedin.com/christopher.morgan"
                    }
                ],
                "soft_skills": [
                    {
                        "value": "Project management"
                    },
                    {
                        "value": "Creative design"
                    },
                    {
                        "value": "Strong decision maker"
                    },
                    {
                        "value": "Innovative"
                    },
                    {
                        "value": "Complex problem solver"
                    },
                    {
                        "value": "Service-focused"
                    }
                ],
                "surnames": [
                    {
                        "value": "Morgan"
                    }
                ]
            },
            "processing_time": 8.661,
            "product": {
                "features": [
                    "document_language",
                    "document_type",
                    "given_names",
                    "surnames",
                    "nationality",
                    "email_address",
                    "phone_number",
                    "address",
                    "social_networks_urls",
                    "profession",
                    "job_applied",
                    "languages",
                    "hard_skills",
                    "soft_skills",
                    "education",
                    "professional_experiences",
                    "certificates"
                ],
                "name": "mindee/resume",
                "type": "standard",
                "version": "1.0"
            },
            "started_at": "2024-10-29T10:21:05.922000"
        },
        "n_pages": 1,
        "name": "cv_templates_with_photo.jpg"
    },
    "job": {
        "available_at": "2024-10-29T10:21:14.791000",
        "error": {},
        "id": "9d15c01c-cc12-4623-8ccb-3a42cdff781d",
        "issued_at": "2024-10-29T10:21:05.922000",
        "status": "completed"
    }
}

You can find the prediction within the prediction key found in document > inference > prediction for document-level predictions: it contains the different fields extracted at the document level, meaning that for multi-pages PDFs, we reconstruct a single object using all the pages.

Extracted data

Using the above Resume example the following are the basic fields that can be extracted.

Document Language

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

{
  "document_language": {
    "value": "ENG"
  }
}

Document Type

document_type: The type of the document sent. Possible values are the following:
- RESUME
- RECOMMENDATION LETTER
- MOTIVATION_LETTER

{
  "document_type": {
    "value": "RESUME"
  }
}

Given Names

given_names: The candidate's first or given names.

{
  "given_names": [
    {
      "value": "Christopher"
    }
  ]
}

Surnames

surnames: The candidate's last names.

{
  "surnames": [
    {
      "value": "Morgan"
    }
  ]
}

Nationality

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

{
  "nationality": {
    "value": null
  }
}

Email Address

email_address: The email address of the candidate.

{
  "email_address": {
    "value": "[email protected]"
  }
}

Phone Number

phone_number: The phone number of the candidate.

{
  "phone_number": {
    "value": "+44 (0)20 7666 8555"
  }
}

Address

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

{
  "address": {
    "value": "177 Great Portland Street, London, W5W 6PQ"
  }
}

social_networks_urls: The list of social network profiles of the candidate.
- name: The name of the social network.
- url: The URL of the social network.

{
  "social_networks_urls": [
    {
      "name": "LinkedIn",
      "url": "linkedin.com/christopher.morgan"
    }
  ]
}

Profession

profession: The candidate's current profession.

{
  "profession": {
    "value": "Senior Web Developer"
  }
}

Job Applied

job_applied: The position that the candidate is applying for.

{
  "job_applied": {
    "value": null
  }
}

Languages

languages: The list of languages that the candidate is proficient in.
- language: The language's ISO 639 code.
- level: The candidate's level for the language.

{
  "languages": [
    {
      "language": "SPA",
      "level": "Fluent"
    },
    {
      "language": "CHI",
      "level": "Beginner"
    },
    {
      "language": "GER",
      "level": "Intermediate"
    }
  ]
}

Hard Skills

hard_skills: The position that the candidate is applying for.

{
  "hard_skills": [
    {
      "value": "HTML5"
    },
    {
      "value": "PHP OOP"
    },
    {
      "value": "JavaScript"
    },
    {
      "value": "CSS"
    },
    {
      "value": "MySQL"
    }
  ]
}

Soft Skills

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

{
  "soft_skills": [
    {
      "value": "Project management"
    },
    {
      "value": "Creative design"
    },
    {
      "value": "Strong decision maker"
    },
    {
      "value": "Innovative"
    },
    {
      "value": "Complex problem solver"
    },
    {
      "value": "Service-focused"
    }
  ]
}

Education

education: The list of the candidate's educational background.
- school: The name of the school.
- degree_type: The type of degree obtained, such as Bachelor's, Master's, or Doctorate.
- degree_domain: The area of study or specialization.
- start_year: The year when the education program or course began.
- start_month: The month when the education program or course began.
- end_year: The year when the education program or course was completed.
- end_month: The month when the education program or course was completed.

{
  "education": [
    {
      "degree_domain": "Computer Information Systems",
      "degree_type": "Bachelor of Science",
      "end_month": null,
      "end_year": null,
      "school": "Columbia University, NY",
      "start_month": null,
      "start_year": "2014"
    }
  ]
}

Professional Experiences

professional_experiences: The list of the candidate's professional experiences.
- employer: The name of the company or organization.
- role: The position or job title held by the candidate.
- department: The specific department or division within the company.
- contract_type: The type of contract for the professional experience.
- start_year: The year when the professional experience began.
- start_month: The month when the professional experience began.
- end_year: The year when the professional experience ended.
- end_month: The month when the professional experience ended.

{
  "professional_experiences": [
    {
      "contract_type": null,
      "department": null,
      "employer": "Luna Web Design, New York",
      "end_month": "05",
      "end_year": "2019",
      "role": "Web Developer",
      "start_month": "09",
      "start_year": "2015"
    }
  ]
}

Certificates

certificates: The list of certificates obtained by the candidate.
- year: The year when a certificate was issued or received.
- provider: The organization or institution that issued the certificate.
- name: The name of certification.
- grade: The grade obtained for the certificate.

{
  "certificates": [
    {
      "grade": null,
      "name": "PHP Framework (certificate):Zend,Codeigniter,Symfony",
      "provider": null,
      "year": null
    },
    {
      "grade": null,
      "name": "Programming Languages:JavaScript,HTML5, PHP OOP,CSS,SQL,MySQL",
      "provider": null,
      "year": null
    }
  ]
}

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Set up the API

API Response

Extracted data

Document Language

Document Type

Given Names

Surnames

Nationality

Email Address

Phone Number

Address

Social Networks

Profession

Job Applied

Languages

Hard Skills

Soft Skills

Education

Professional Experiences

Certificates