ResuScan AI-Based Resume Parser with ATS Score

Abstract

In the current scenario of the job market, hundreds of resumes come in for a single position, and it is practically impossible to get the best candidates manually. ResuScan is an AI-driven system that automates this process using Natural Language Processing and Machine Learning. The system extracts important details, such as skills, education, and experience from resumes, compares them with job requirements, and generates an ATS score. It aids recruiters in shortlisting candidates more precisely and rapidly. Along with resume screening, ResuScan performs AI-driven skill assessments of candidates pertaining to practical skills. One of the strongest features of this system is its ability to provide personalized feedback to rejected candidates, suggesting other job roles or companies that would be a good fit for their skillset. The system recommends learning pathways to help users upgrade and become job-ready. Therefore, ResuScan simplifies automation by including intelligent feedback, reducing manual effort, increasing fairness in hiring, and bridging the gap in recruitment for companies and candidates in a transparent manner.

Introduction

The text describes ResuScan, an AI-driven resume parsing and scoring system designed to improve the efficiency and fairness of recruitment. Traditional resume screening is time-consuming, prone to bias, and often overlooks qualified candidates due to keyword mismatches.

ResuScan uses Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically extract key information from resumes, such as skills, education, and experience. It compares this data with job requirements and generates an ATS (Applicant Tracking System) score to evaluate how well a candidate fits a role.

A key feature of ResuScan is its candidate-centric approach, where it not only ranks and shortlists applicants but also provides personalized feedback, skill improvement suggestions, and alternative job recommendations. This helps both recruiters and job seekers.

The system methodology includes resume collection and preprocessing, information extraction using NLP techniques, semantic matching with job descriptions, and multi-dimensional scoring based on skills, experience, and keywords. These scores are combined into a Candidate Proficiency Score (CPS) for accurate ranking.

Additionally, the system offers a recruiter dashboard for insights and integrates modern technologies like Python, FastAPI, Firebase, and NLP libraries.

Conclusion

The AI Resume Analyzer project explains how AI and NLP can make a difference in hiring. It does so by automating the resume-checking process, saving a lot of time and effort for recruiters in an organization. Instead of reading every resume manually, the system quickly analyzes them and generates unbiased, data-based results for every candidate. Making the initial stage of hiring easier and faster, it also provides useful suggestions and insights helpful for both recruiters and job seekers. There is still some scope for improvements regarding different formats of resumes and reduction of bias in the system itself, but that can be achieved over time with updates and new technologies. Al-based ATS automates resume parsing, keyword matching, and candidate ranking to work through thousands of applications and find the most relevant profiles.

References

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Copyright

Copyright © 2026 Pratik S. Yawale, Shweta Bendke, Prajakta Morey, Poonam Buchake, EshwarPrasad Ramelliwar, Tejas Ingle. 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.