Resume Parser API

Resume Parser API

Parses any resume/CV into JSON text using Natural Language Processing (NLP) techniques.

Response example. If you want to see more check the documentation.

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Resume parsing API is a hosted service that takes a resume as an input that can be in PDF or MS Word format, then convert it into a structured JSON data format.

Resume in JSON format
Use cases
Extract data from any resume/CV to:
  • Search, Sort and Store resumes
  • Enhance your talent database.
  • Save time and efforts.
  • Eliminate manual intervention. Eliminate privacy issues.
  • Faster closing of open positions

Many of our users use our API for building usable datasets for an AI based job matching tool.

Resume Parser API is well tested for English language and works generates somehow acceptable results for 12 more most common languages. We have trained the parser model with more than 26.000 collage/university names and 70.000+ skills. using best in class NLP techniques, we are capable of parsing any resume/CV format out there. and try for yourself. (No credit cards needed until you upgrade to a paid plan).

Parsing a CV/resume on a URL

First, start by (free tier available), get your API key and you are ready. Next, you can test using the following cURL command. This code will fetch the sample resume in docx format located at https://.../sample_resume.docx

curl --location --request GET '' \
--header 'apikey: YOUR API KEY HERE'

The results will be as follows

    "name": "JOHN DOE",
    "email": "[email protected]",
    "skills": [
        "Computer science",
    "education": [
            "name": "RIVER BROok university",
            "dates": "2011"
    "experience": [
            "title": "Front End Developer",
            "dates": "2015",
            "location": "Camarillo",
            "organization": "DP TECHNOLOGY CORP."
            "title": "Front End Developer",
            "dates": "2015",
            "location": "Plano",
            "organization": "VOKO COMMUNICATIONS"

Parsing a CV/resume from local file

The procedure is pretty similar to what we do for parsing a resume on a URL. First, simply (free tier available), get your API key and you are done.

Use the following code to upload a resume from your computer to the service and get the resume parsed in JSON format immediately back.

curl --location --request POST '' \
--header 'Content-Type: application/octet-stream' \
--header 'apikey: YOUR API KEY HERE' \
--data-binary '@/C:/Users/test/Desktop/sample_resume.docx'

Bulk processing of resumes

If you need to parse a huge number of resumes at once, please contact us at [email protected] for competitive discounts and fanatical support.

Security and Privacy

We never store any private information on servers. The resume files uploaded to our servers are parsed and automatically deleted after 15 seconds. We never store the parsed data, nor the resume files in any of our servers and we don't share this information with any of our partners. APILayer is a business located in European Union and we totaly respect our users' privacy concerns. Check our GDPR compliance, Privacy Policy statements for more information.

If you need written consent for regulative purposes, we may arrange a signed and sealed agreement between two parties.

The service won't parse my resume!

Please note that, resume parsing depends on predictive models and can not be 100% accurate. We do our best and enhance the API's capabilities frequently. If you have any problem with a special resume/cv format, please let us know. We'll fix it fast!

Why is the price so competitive?

We are a small but neat startup company without the huge expenses of our competitors. We work harder than any other competitor in the market and try to spread the word to a bigger audience. So we keep our price lower than most of our competitors, offering the optimum price/quality ratio in the market.

and subscribe to the service. If you need a custom plan get in touch with us.

Build your own Resume Parser Using Python and NLP
Blog Article
Build your own Resume Parser Using Python and NLP