AI-Based Resume Screening System Using Natural Language Processing

Recruitment processes increasingly rely on automation due to high application volumes. Manual resume screening is time-consuming, subjective, and prone to bias. This research-based project focuses on developing an AI-powered resume screening system using Natural Language Processing to improve efficiency, consistency, and fairness in candidate shortlisting.

Problem Statement

Organizations receive thousands of resumes for a single role. Human screening leads to delays, fatigue, and inconsistent decision-making. There is a need for an intelligent system that can evaluate resumes objectively against job requirements.

Research Objectives

The project aims to analyze resumes using NLP techniques, extract relevant skills, experience, and education, and rank candidates based on job descriptions. It also explores bias mitigation strategies within automated screening.

Methodology

Resumes are parsed using text preprocessing techniques such as tokenization, stop-word removal, and lemmatization. Skill extraction is performed using named entity recognition and keyword matching. Similarity scoring is applied using vector embeddings.

Technologies Used

The system integrates Python, NLP libraries, machine learning classifiers, and a web-based dashboard for visualization.

Expected Outcomes

The system reduces screening time, improves consistency, and provides explainable candidate rankings. It demonstrates real-world applicability in HR automation.

Conclusion

This project bridges AI research with practical recruitment challenges, making it suitable for academic evaluation and industry deployment.