AI-Powered Intelligent Resume Screening and Enhancement System

Abstract

In today\'s highly competitive job market environment, there are quite a number of challenges that can arise for both parties when the process of recruitment is carried out. For example, recruiting personnel have to manually analyze large numbers of resumes, which is a cumbersome process. On the other hand, candidates face the challenge of knowing why their resumes were rejected and ways of improving them. Therefore, this paper seeks to come up with an AI-powered intelligent resume screening and enhancement system. Such an application will consist of two parts: machine learning-based selection of candidates and personalized resume analysis via NLP. This system will use NLP approaches to extract structured data from unstructured resumes. Afterward, job role and resume matching will be conducted followed by predicting the probability of a candidate making it to the interview based on their score. In addition, it will do a skill gap analysis, quality rating of resumes, and identification of the required competencies to fill the vacancy. The solution will further incorporate user-friendly features that will ensure that no form of bias is experienced during the entire process.

Introduction

The text presents an AI-powered intelligent resume screening and enhancement system designed to improve modern recruitment processes using NLP and machine learning.

It begins by explaining problems in traditional Applicant Tracking Systems (ATS), such as over-reliance on keyword matching, lack of transparency, and biased or time-consuming manual resume screening. These limitations often lead to qualified candidates being overlooked. To solve this, the proposed system uses advanced NLP and transformer-based models to understand the semantic meaning of resumes and job descriptions rather than just matching keywords.

The system architecture includes several stages: resume and job description input (PDF/DOCX/OCR), text extraction, NLP preprocessing, skill extraction (dictionary matching, lemmatization, and NER), and semantic similarity analysis using models like BERT or Sentence Transformers. It also includes a Jaccard-based pre-screening step for efficiency and a rule-based ATS scoring engine that evaluates formatting, completeness, and skill relevance.

Additionally, the system generates skill gap analysis, personalized improvement suggestions, and visual dashboards. It also produces downloadable PDF reports to guide candidates in improving their resumes. The workflow is designed to simulate real-world recruitment while offering both candidates and recruiters better insights.

The literature review highlights the evolution from rule-based ATS and traditional NLP techniques to modern transformer-based semantic models, which significantly improve matching accuracy. However, existing systems still lack explainability and actionable feedback, which this work aims to address.

In summary, the proposed system combines NLP, semantic embeddings, ATS simulation, and visualization tools to create a more intelligent, fair, and user-friendly recruitment platform that improves both resume quality and hiring decisions.

Conclusion

The AI-Powered Perspicacious Resume Screening and Enhancement System successfully addresses significant inefficiencies in today\'s recruitment industry. The system combines Natural Language Processing (NLP) and Machine Learning to enhance the ability to go beyond just matching keywords and conduct in-depth semantic analysis of candidates\' skills and experiences. This transparency and fairness in the ranking process are achieved by incorporating Explainable AI (XAI) using LIME and SHAP, which ensures that the recruiters receive clear explanations for the scores of the candidates. Moreover, the system connects recruiters with job seekers by leveraging Generative AI to offer customized suggestions and expert gaps analysis. This two-way process not only simplifies the hiring process for companies, but it also empowers job seekers to improve their resumes. Future improvements may involve real-time connectivity with career sites and extend the system to bolster additional specific sectors.

References

[1] D. Jurafsky and J. H. Martin, Verbalization and Language Processing, 3rd ed., Pearson, 2023. [2] T. Mikolov et al., “Efficient Estimation of Word Representations in Vector Space,” arXiv preprint arXiv:1301.3781, 2013. [3] J. Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL-HLT, 2019. [4] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings utilizing Siamese BERT-Networks,” in Proceedings of EMNLP, 2019. [5] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [6] M. Honnibal and I. Montani, “spaCy 2: Natural Language Understanding with Bloom Embeddings,” Explosion AI, 2017. [7] G. Salton and C. Buckley, “Term-weighting Approaches in Automatic Text Retrieval,” Information Processing & Management, 1988. [8] C. D. Manning, P. Raghavan, and H. Schütze, Exordium to Information Retrieval, Cambridge University Press, 2008. [9] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python, O’Reilly Media, 2009. [10] Hugging Face, “Transformers Library Documentation,” [Online]. Available: https://huggingface.co/docs/transformers [11] Streamlit Inc., “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io [12] Plotly Technologies Inc., “Plotly Python Graphing Library,” [Online]. Available: https://plotly.com/python/ [13] ReportLab, “ReportLab PDF Library Documentation,” [Online]. Available: https://www.reportlab.com/docs/ [14] Jaided AI, “EasyOCR Documentation,” [Online]. Available: https://www.jaided.ai/easyocr/ [15] Karthi-1211, “Skill Gap AI – Resume Perspicacity System,” [Online]. Available: https://github.com/Karthi-1211/Adeptness-Gap-AI [16] T. Joachims, “Text Categorization with Support Vector Machines: Learning with Many Pertinent Features,” in Proceedings of ECML, 1998. [17] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [18] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, pp. 273–297, 1995. [19] M. Ribeiro, S. Singh, and C. Guestrin, “Why Should I Trust You? Explicating the Prognostications of Any Classifier,” in Proceedings of KDD, 2016. [20] S. Lundberg and S.-I. Lee, “A Cumulated Approach to Interpreting Model Presages,” in Proceedings of NIPS, 2017.

Copyright

Copyright © 2026 Neha Rama Gavali, Unaati Sudhakar Surywanshi, Nayan Sambhaji Patil, Niranjan Ravindra Patil, Sheetal Prashant Patil. 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.