International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
www.irjet.net
Talent Lens – AI-Based Resume and Job Description Matching System for HR Automation Ms. T.P Kamatchi ¹, Sri Vishnu A M ², Siddharth M ³, Gokul Nanda M⁴, Mukilan M⁵ ¹ Head of Department, Department of Computer Engineering, PSG Polytechnic College, Coimbatore, Tamil Nadu, India 2,3,4,5 Final Year Diploma Student, Department of Computer Engineering, PSG Polytechnic College, Coimbatore, Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.1 RESUME EVALUATION AND JOB Abstract - Recruitment is a complex and time-consuming DESCRIPTION MATCHING process due to its high application count, unstructured resume format, and imprecise assessment methods [1],[18]. The traditional approach toward resume screening may result in inept candidate evaluation and undervalued talent discovery [5],[14]. Talent Lens is an AI-driven resume and job description matching tool designed to fully automate and optimize the recruitment process [3],[6]. This tool uses Natural Language Processing and machine learning to extract important information such as skills, education, work experience, and certifications from resumes and job descriptions [4],[15]. This tool calculates semantic similarities for objective ranking and matching of apt talent for respective job roles [1], [9], [20]. Talent Lens will act like an intelligent decision aid for HR analysts by allowing ranked candidate listing, skill gap analysis, and data visualization that may assist in informed recruitment decisions. This automated resume analysis and candidate ranking tool reduces manual processing efforts and promotes bias-free and efficient recruitment [14], [21]. The proposed model drastically accelerates and aims to achieve better accuracy of hiring speed and results in discovering apt talent in short time spans.
The received resumes are automatically processed for extracting key features such as skills, education, and experience using NLP techniques. Each received resume is then matched with the job description and, if available, the model CV, which is an ideal representation of the candidate profile [5]. Semantic similarity measurements are calculated, and a ranked list of candidates is obtained based on which the recruiter can easily shortlist candidates and view the gaps in skills, thus shortlisting candidates quickly and accurately [13].
1.2 RESUME PROCESSING AND BUILDER MODULE This module enables the uploading of the resume in any form or the use of pre-set templates. All the necessary details such as personal details, educational background, skills, projects, certifications, and work experience are extracted and formatted in a standardized manner [15]. Hence, the data is formatted in a manner that enables the accurate matching process by AI algorithms and also helps in reporting in the later stage in the TalentLens System [17].
1. INTRODUCTION Contemporary recruitment practices entail the submission of many resumes in response to each job advertisement. Such manual evaluation takes time. Un-structurally arranged resumes, unstructured content, and subjective resume evaluation contribute to the ineffectiveness of resume screening [18].
1.3 OBJECTIVES
In order to solve the above-mentioned challenges, the "Talen t Lens – AI Based Resume & Job Description Matching System for HR Automation" is proposed. Talent Lens is an AIbased system that uses Natural Language Processing techniques to analyze the resume and job description [3]. This system is capable of extracting data such as skills, education, and work experience from the resume and job description [4]. Based on this data, similarity metrics are calculated to rank the candidate as per the similarity to the job requirement [1]. Instead of replacing the recruiter, the Talent Lens system is used as an assistant tool that can enhance the accuracy of this process and speed it up [19].
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Impact Factor value: 8.315
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In order to automatically screen and shortlist resumes for recruiters To convert the unstructured format of resumes into a structured one to extract For comparing candidate profiles and job descriptions based on AI analysis To rank candidates on the basis of their semantic similarity and skills relevance [20] To offer analysis and recommendations on improvement in the skill gap [13] For facilitating team collaboration through shared shortlists [24] To minimize the routine screening process as well as the chances of judgment bias in the For facilitating an AI-driven recruitment assistant service [14], [12]
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