Resume Screening Automation with NLP Techniques

Abstract

This project introduces an automated solution designed to simplify and enhance the resume screening process
using Natural Language Processing (NLP) techniques. The system is capable of extracting essential details from resumes,
including skills, work experience, and educational qualifications, to effectively match applicants with job requirements. By
utilizing advanced NLP models, the system interprets and ranks resumes according to their relevance, thereby minimizing
the time and manual effort required by recruiters. The proposed approach applies NLP methods to analyze unstructured
resume data and convert it into a structured summary by identifying key attributes such as skills, education, and
professional background. By filtering out unnecessary or irrelevant information, the system makes the screening process
more efficient, allowing recruiters to evaluate candidates more quickly and accurately. After completing the text
processing stage, the system implements a vectorization technique along with cosine similarity to compare resumes with
job descriptions. Based on the similarity scores generated, candidates are ranked according to how well they match the job
criteria. This ranking assists recruiters in identifying the most suitable applicants for a given position. The system is
designed to enhance the precision of candidate selection while promoting a faster and less biased hiring process. The
output is displayed through an intuitive interface, presenting a ranked list of candidates along with extracted details and
matching scores, enabling recruiters to make informed decisions with ease. Overall, this tool significantly reduces the
effort and time involved in the initial screening phase, thereby improving the overall efficiency of recruitment