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@article{204484,
        author = {Raman and Pramod Lodhi and Riya Saini and Priya Chauhan},
        title = {Resume Screening System Using NLP},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4539-4548},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204484},
        abstract = {In recent years, the recruitment industry has experienced rapid digital transformation due to the increasing number of job applications and the growing need for efficient hiring processes. Traditional resume screening methods rely heavily on manual evaluation performed by Human Resource (HR) professionals. Such methods are time-consuming, inconsistent, expensive, and often affected by unconscious human bias. Organizations receiving thousands of resumes for a single job opening face major challenges in identifying suitable candidates within limited timeframes.
Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies have emerged as powerful solutions for automating recruitment-related tasks. NLP enables computers to understand, analyze, and process textual information present in resumes and job descriptions. By using NLP techniques, organizations can automatically extract candidate skills, qualifications, experience, certifications, and educational details from resumes and compare them with job requirements.
This research paper presents a detailed Resume Screening System using NLP that automates candidate shortlisting and ranking. The proposed system utilizes text preprocessing, feature extraction, TF-IDF vectorization, and cosine similarity to identify the relevance between resumes and job descriptions. The system significantly improves recruitment efficiency, reduces manual workload, minimizes bias, and supports accurate candidate selection. The paper also discusses system architecture, implementation techniques, algorithms, advantages, limitations, future enhancements, and experimental outcomes.},
        keywords = {Artificial Intelligence, Natural Language Processing, Resume Screening, Recruitment Automation, Machine Learning, TF-IDF, Cosine Similarity, Candidate Ranking.},
        month = {June},
        }