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ResumeParser : Advanced Resume Parsing Challenge (by Revelio Labs)

Problem Statements

Primary Challenge:

Develop a comprehensive resume parser, "ResumeRevealer," capable of extracting detailed information from resumes in various formats (PDF, JPG, HTML, DOC, etc.). The parser should accurately classify text into distinct sections (e.g., education, work experience, skills) and sequence them based on dates, where available.

Standardization Challenge:

Enhance the "ResumeRevealer" to standardize different job titles and occupations against the O-NET database, ensuring a consistent taxonomy across parsed resumes.

Skill Extraction

Utilizing natural language processing techniques, we extract detailed skills and competencies from resumes. In addition to parsing, the system also employs heuristic NLP techniques such as using regular expressions (regexes) to capture skills mentioned in the resumes.

Installation and Setup

Requirements

  • Python 3.x
  • TensorFlow
  • Keras
  • scikit-learn

Installation

  1. Clone this repository: git clone https://github.com/your-username/ResumeRevealer.git
  2. Navigate to the project directory: cd ResumeRevealer
  3. Install dependencies: pip install -r requirements.txt

Usage

  1. Ensure you have resumes to parse. Examples are provided in the repository. Adjust the file paths in app.py accordingly to use the provided resumes.

  2. Add the path to the PDF resume you want to parse in app.py:

    # GIVE ABSOLUTE PATH TO THE RESUME HERE
########################
resume_path = 'your_resume.pdf'
########################

  3. Run the parser: python app.py

  4. The output will be created and stored in output.json file having comprehensive details Job Title: The title of the job position. Company: The name of the company where the job was held. Start Date: The start date of the job. End Date: The end date of the job (if available). Education School Name: The name of the educational institution. Field of Study: The field of study or major. Qualification: The qualification or degree obtained. Skills A list of extracted skills and competencies relevant to the candidate's profile.

    Contact Info Email: The candidate's email address. phone1: The candidate's primary phone number. phone2: An additional phone number if available. address: The candidate's address if available. Name The name of the candidate.

    Standardized Job Titles A list of job titles standardized against the O-NET database, ensuring consistency and compatibility with industry standards.

Features

Text Segmentation

Automatically split the text into different parts such as education, previous work experience, skills, contact information, and job titles. The segmentation is done sequentially according to the timeline.

Standardization of Job Titles

The project includes functionality to standardize job titles against the O-NET database. This ensures consistency and accuracy in categorizing resumes.

Skill Extraction

Utilizing advanced natural language processing techniques, ResumeRevealer extracts detailed skills and competencies from resumes. This feature enhances candidate evaluation by highlighting specific abilities relevant to job roles.

Matching Job Titles to O-NET Database

A machine learning script matches the obtained job titles to a standardized job list from the O-NET database, ensuring consistency and compatibility with industry standards.

Acknowledgments

  • Special thanks to Revelio Labs for their track of Advanced Resume Parsing Challenge for providing the problem statements and inspiration for this project.
  • Thanks to Centre of Excellence in Data Science (Nirma University) for organizing the MINeD hackathon.
  • Special thanks to Praxal Patel from Revelio Labs (the track sponsor) for his help in understanding the statement.
  • Credits to the developers of TensorFlow, Keras, and scikit-learn for their invaluable tools and libraries.