pyresparser

A simple resume parser used for extracting information from resumes

---

Features

• Extract name
• Extract email
• Extract mobile numbers
• Extract skills
• Extract total experience
• Extract college name
• Extract degree
• Extract designation
• Extract company names

Installation

• You can install this package using

pip install pyresparser

• For NLP operations we use spacy and nltk. Install them using below commands:

# spaCy
python -m spacy download en_core_web_sm

# nltk
python -m nltk.downloader words
python -m nltk.downloader stopwords

Supported File Formats

• PDF and DOCx files are supported on all Operating Systems
• If you want to extract DOC files you can install textract for your OS (Linux, MacOS)
• Note: You just have to install textract (and nothing else) and doc files will get parsed easily

Usage

• Import it in your Python project

from pyresparser import ResumeParser
data = ResumeParser('/path/to/resume/file').get_extracted_data()

CLI

For running the resume extractor you can also use the cli provided

usage: pyresparser [-h] [-f FILE] [-d DIRECTORY] [-r REMOTEFILE]
                   [-re CUSTOM_REGEX] [-sf SKILLSFILE] [-e EXPORT_FORMAT]

optional arguments:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  resume file to be extracted
  -d DIRECTORY, --directory DIRECTORY
                        directory containing all the resumes to be extracted
  -r REMOTEFILE, --remotefile REMOTEFILE
                        remote path for resume file to be extracted
  -re CUSTOM_REGEX, --custom-regex CUSTOM_REGEX
                        custom regex for parsing mobile numbers
  -sf SKILLSFILE, --skillsfile SKILLSFILE
                        custom skills CSV file against which skills are
                        searched for
  -e EXPORT_FORMAT, --export-format EXPORT_FORMAT
                        the information export format (json)

Notes:

• If you are running the app on windows, then you can only extract .docs and .pdf files

Result

The module would return a list of dictionary objects with result as follows:

[
  {
    'college_name': ['Marathwada Mitra Mandal’s College of Engineering'],
    'company_names': None,
    'degree': ['B.E. IN COMPUTER ENGINEERING'],
    'designation': ['Manager',
                    'TECHNICAL CONTENT WRITER',
                    'DATA ENGINEER'],
    'email': 'omkarpathak27@gmail.com',
    'mobile_number': '8087996634',
    'name': 'Omkar Pathak',
    'no_of_pages': 3,
    'skills': ['Operating systems',
              'Linux',
              'Github',
              'Testing',
              'Content',
              'Automation',
              'Python',
              'Css',
              'Website',
              'Django',
              'Opencv',
              'Programming',
              'C',
              ...],
    'total_experience': 1.83
  }
]

References that helped me get here

• Some of the core concepts behind the algorithm have been taken from https://github.com/divapriya/Language_Processing which has been summed up in this blog https://medium.com/@divalicious.priya/information-extraction-from-cv-acec216c3f48. Thanks to Priya for sharing this concept

• https://www.kaggle.com/nirant/hitchhiker-s-guide-to-nlp-in-spacy

• https://www.analyticsvidhya.com/blog/2017/04/natural-language-processing-made-easy-using-spacy-%E2%80%8Bin-python/

• Special thanks to dataturks for their annotated dataset

Stargazer over time