AI Resume Scanner and Candidate Ranker: An Intelligent Framework for Automated Recruitment Screening

Abstract

The rapid growth of online recruitment platforms has significantly increased the volume of job applications received by organizations. Manual resume screening has become a time-consuming, repetitive, and error-prone task that often delays the recruitment process and affects hiring efficiency. To address these challenges, this paper presents an AI Resume Scanner and Candidate Ranker, an intelligent recruitment support framework designed to automate resume analysis and candidate shortlisting. The proposed system utilizes Natural Language Processing (NLP) techniques and a weighted scoring mechanism to extract candidate information from resumes and compare it against predefined job requirements. The framework evaluates candidates based on multiple criteria, including technical skills, educational qualifications, work experience, and keyword relevance. The system is implemented using React.js, Node.js, Express.js, and MySQL, providing an integrated platform for resume upload, candidate ranking, dashboard visualization, and report generation. Experimental evaluation demonstrates that the proposed approach significantly reduces recruiter workload while improving transparency and consistency in candidate selection. The framework serves as a practical decision-support tool that assists recruiters in identifying qualified candidates efficiently while maintaining human oversight during final hiring decisions.

Introduction

Recruitment today involves handling large volumes of job applications, making manual resume screening slow, inconsistent, and inefficient. While existing Applicant Tracking Systems (ATS) use AI and NLP to automate screening, many rely on simple keyword matching and lack transparency in how candidates are ranked.

This project proposes an AI Resume Scanner and Candidate Ranker that automates resume parsing, evaluates candidates against job requirements, and produces transparent ranking scores to support recruiter decision-making rather than replace it.

Key Objectives

The system aims to:

• Automate resume extraction and screening
• Match candidate profiles with job requirements
• Generate explainable ranking scores
• Reduce manual effort and improve efficiency
• Ensure scalability and security in recruitment

System Approach

The framework uses a layered architecture:

• Frontend: React.js interface for recruiters
• Backend: Node.js and Express.js for processing logic
• Processing Layer: Resume parsing, information extraction, and scoring
• Database: MySQL for storing resumes, jobs, and results

Workflow

Recruiters create job descriptions, candidates upload resumes (PDF/DOCX), and the system extracts key details such as skills, education, and experience. These are compared with job requirements to generate suitability scores, which are then used to rank candidates.

Ranking Method

Candidates are evaluated using a weighted scoring model:

• Skill Score (50%)
• Experience Score (25%)
• Keyword Relevance (15%)
• Education Score (10%)

Final scores determine candidate ranking in descending order, enabling efficient shortlisting.

System Performance

Testing showed:

• Fast resume parsing (1–3 seconds per resume)
• Quick ranking (<1 second)
• Efficient report generation (<2 seconds)
• Successful handling of multiple users

Results and Findings

Compared to manual screening and traditional ATS systems, the proposed framework:

• Speeds up recruitment significantly
• Improves transparency through explainable scoring
• Maintains scalability and recruiter control

Conclusion

This paper presented an AI Resume Scanner and Candidate Ranker for automated recruitment screening. The proposed framework combines resume parsing, candidate evaluation, and transparent ranking mechanisms to improve recruitment efficiency. By integrating NLP-inspired processing techniques with weighted scoring algorithms, the system reduces manual workload and accelerates candidate shortlisting. The implementation demonstrates that intelligent automation can significantly improve recruitment workflows while maintaining transparency and fairness. The framework serves as a practical decision-support system rather than a replacement for human recruiters. Future research will focus on incorporating transformer-based semantic matching, OCR support for scanned resumes, multilingual candidate processing, fairness evaluation, recruiter feedback learning, interview scheduling integration, and advanced analytics dashboards.

References

[1] MDN Web Docs, “HTML: HyperText Markup Language,” Available: https://developer.mozilla.org [2] MDN Web Docs, “CSS Reference,” Available: https://developer.mozilla.org [3] MDN Web Docs, “JavaScript Guide,” Available: https://developer.mozilla.org [4] React Documentation, “React: The Library for Web and Native User Interfaces,” Available: https://react.dev [5] Node.js Documentation, “Node.js Runtime Environment,” Available: https://nodejs.org [6] Oracle Corporation, “MySQL Reference Manual,” Available: https://dev.mysql.com [7] Tailwind Labs, “Tailwind CSS Documentation,” Available: https://tailwindcss.com [8] M. Saatci, R. Kaya, and R. Unlu, “Resume Screening with Natural Language Processing,” 2024. [9] Z. Chen, “Ethics and Discrimination in Artificial Intelligence-Enabled Recruitment Practices,” Humanities and Social Sciences Communications, vol. 10, no. 1, pp. 1–12, 2023. [10] “Automated Resume Screening System Using NLP and Keyword Matching,” International Journal of Engineering Research and Technology (IJERT), 2026.

Copyright

Copyright © 2026 Rajeshwari N, Nischitha M, Deeksha M R, Deeksha R, Devika P C. 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.