Automated Resume Parser 📄➡️📊

A Python-based tool for extracting structured data from resumes in PDF, DOCX, and TXT formats using NLP and pattern matching.

Features ✨

• Multi-format Support: Process PDF, DOCX, and TXT files
• Core Information Extraction:
  • Contact details (name, email, phone, social links)
  • Education history and qualifications
  • Work experience timelines
  • Technical skills and certifications
• Smart Section Detection: Identifies common resume sections
• Output Formats: JSON and CSV exports
• Batch Processing: Handle multiple files simultaneously

Installation 🛠️

git clone https://github.com/DarshanCode2005/resume-parser.git cd resume-parser pip install -r requirements.txt

Requirements 📦

• Python 3.8+
• spaCy (en_core_web_sm model)
• pdfminer.six
• python-docx
• pandas

Usage 🚀

from resume_parser import ResumeParser

Single file processing parser = ResumeParser() parsed_data = parser.parse_resume("resumes/john_doe.pdf")

Batch processing parser.process_directory("input_resumes/", "output_data/")

Output Structure 📂

{ "contact_information": { "name": "John Doe", "email": "john@example.com", "phone": "+1-555-123-4567", "linkedin": "linkedin.com/in/johndoe" }, "education": [ "BSc Computer Science - University of Tech (2018-2022)" ], "skills": [ "Python", "Machine Learning", "AWS" ], "certifications": [ "AWS Certified Developer - 2023" ] }

Key Technologies 🔍

Component	Technology Used
NLP Processing	spaCy NER Model
Text Extraction	pdfminer, python-docx
Data Structuring	JSON/CSV serialization
Pattern Matching	Regular Expressions

Roadmap 🗺️

• Add AI-powered section interpretation
• Implement resume scoring system
• Support image-based resumes (OCR)
• Multi-language support
• REST API integration

Contributing 🤝

We welcome contributions! Please see our Contribution Guidelines for details.

License 📄

MIT License - See LICENSE for details