The increasing number of online job applications has made candidate screening a time-consuming and challenging task for recruiters. Manual evaluation of resumes often requires significant effort and may lead to inconsistencies in candidate selection. To address these challenges, this paper presents an Automated Resume Screening System developed using Natural Language Processing (NLP) techniques. The proposed system automates resume analysis by extracting skills from PDF resumes, comparing them with job descriptions, and supporting recruitment decision-making through intelligent candidate evaluation. The system provides separate functionalities for recruiters and job seekers through an interactive web-based interface. Recruiters can upload candidate resumes and job descriptions to evaluate candidate suitability, while job seekers can receive job recommendations based on their extracted skills. The proposed system utilizes SpaCy-based NLP techniques, including tokenization, lemmatization, stop-word removal, and text preprocessing, to identify relevant skills from resumes and job descriptions. It subsequently calculates a resume-job match score, identifies matched and missing skills, classifies candidate suitability, and recommends appropriate job roles. The proposed solution has been implemented using Python and deployed as an interactive web application using Streamlit. Experimental evaluation demonstrates the effectiveness of the system using standard performance metrics, including accuracy, precision, recall, F1-score, and average processing time. By automating resume screening and candidate evaluation, the proposed framework reduces manual effort, improves recruitment efficiency, and demonstrates practical application of NLP techniques in intelligent recruitment systems. Keywords: Natural Language Processing (NLP); Streamlit; Recruitment Automation; Resume Parsing; Resume Screening; Skill Extraction; Candidate Evaluation; Job Recommendation
Biswas et al. (Wed,) studied this question.