Resume Analyser Using NLP

Abstract

Resume Analyser Using Natural Language Processing (NLP) is an intelligent system developed to automate resume screening and candidate evaluation. The system extracts important details such as skills, education, projects, certifications, and experience from resumes and compares them with job descriptions using Machine Learning and NLP techniques. TF-IDF vectorization and cosine similarity are used to calculate ATS scores and identify matching and missing skills. The proposed system improves recruitment efficiency, reduces manual effort, and helps candidates improve resume quality for better job opportunities.

Introduction

The Resume Analyser Using NLP is an intelligent recruitment support system designed to automate resume screening and improve hiring efficiency. Traditional resume evaluation methods are manual, time-consuming, and prone to errors, while many existing Applicant Tracking Systems (ATS) rely mainly on keyword matching and provide limited analysis. To overcome these limitations, the proposed system uses Natural Language Processing (NLP) and Machine Learning techniques to analyze resumes more effectively.

The system automatically extracts information from resumes, compares candidate profiles with job descriptions, calculates ATS scores, identifies matching and missing skills, and generates personalized suggestions for resume improvement. This benefits both recruiters, by reducing screening time, and job seekers, by enhancing resume quality and job readiness.

The architecture consists of several modules, including resume upload, text extraction, preprocessing, skill extraction, ATS scoring, candidate ranking, visualization, and output generation. Uploaded resumes are processed using NLP techniques such as tokenization, stop-word removal, and text cleaning. The system then applies TF-IDF vectorization and cosine similarity to measure the semantic similarity between resumes and job descriptions.

The methodology follows a structured process: resume upload, text extraction from PDF files, preprocessing, skill identification, vectorization, similarity calculation, ATS score generation, and result visualization. ATS scores are calculated based on skill matching, keyword relevance, and overall resume quality.

The implementation is developed using Python and Streamlit. NLP tasks are handled using NLTK, while machine learning operations such as TF-IDF and cosine similarity are performed using Scikit-learn. Data processing utilizes Pandas and NumPy, and visualizations are created using Plotly and Matplotlib.

The results demonstrate that the system can accurately analyze resumes, evaluate candidate-job compatibility, identify missing skills, and rank multiple candidates effectively. Interactive dashboards, charts, gauges, and word clouds help users understand the analysis clearly and make informed recruitment decisions.

Future enhancements include integrating deep learning and GPT-based models, adding OCR support for image-based resumes, enabling cloud deployment, connecting with real-time recruitment portals, and supporting multilingual resume analysis. Overall, the proposed system offers a smart, automated, and scalable solution for modern recruitment and resume evaluation.

Conclusion

The Resume Analyser Using NLP provides an intelligent solution for automated recruitment systems. By integrating NLP and Machine Learning techniques, the system improves hiring accuracy, reduces recruiter workload, and helps candidates improve their resumes for better ATS compatibility. The proposed system is efficient, scalable, and highly relevant for modern recruitment industries.

References

The Resume Analyser Using NLP provides an intelligent solution for automated recruitment systems. By integrating NLP and Machine Learning techniques, the system improves hiring accuracy, reduces recruiter workload, and helps candidates improve their resumes for better ATS compatibility. The proposed system is efficient, scalable, and highly relevant for modern recruitment industries.

Copyright

Copyright © 2026 Shravya M S, Manya S S, Poornima H V, Rakshitha M K, Pallavi M R, Mrs. Madevamma S M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.