Authors : Dr. G. Sravan Kumar; K. Varshitha; K. Keerthi; N. Vikesh

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/k642szd7

DOI : https://doi.org/10.38124/ijisrt/25may1909

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This project presents an automated system to streamline resume screening using Natural Language Processing (NLP) techniques. The system extracts key information from resumes, such as skills, experience, and qualifications, to efficiently match candidates to job descriptions. By leveraging NLP models, this tool can understand and rank resumes based on relevance, significantly reducing time and effort for recruiters. Our system uses Natural Language Processing to extract relevant information like skills, education, experience, etc. from the unstructured resumes and hence creates a summarized form of each application. With all the irrelevant information removed, the task of screening is simplified and recruiters are able to better analyze each resume in less time. After this text mining process is completed, the proposed solution employs a vectorisation model and uses cosine similarity to match each resume with the job description. The calculated ranking scores can then be utilized to determine best-fitting candidates for that particular job opening. The system aims to improve accuracy in candidate selection, ensuring a faster, more unbiased hiring process. The results are presented in a user-friendly interface, displaying a ranked list of candidates along with their extracted information and match percentages, enabling recruiters to quickly identify the most promising applicants. This tool significantly reduces the time and effort required for initial resume screening, improving the efficiency of the hiring process.

Keywords : Candidate Screening, Skills Matching, Experience Analysis, Recruitment Efficiency, Hiring Process Optimization, Automated Candidate Selection, Resume Parsing.

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