Resume Analyzer and ATS scoring system

Abstract

The rapid growth of digital recruitment and the increasing adoption of Applicant Tracking Systems (ATS) have significantly transformed the hiring process across industries. While academic institutions successfully equip students with technical knowledge and professional skills, many graduates face difficulties in securing employment due to poorly optimized resumes that fail to meet ATS requirements. This research presents a comprehensive study of the design, architecture, implementation, deployment, and evaluation of "Resume Analyzer and ATS Scoring System," a web-based intelligent recruitment support platform developed as a Bachelor of Computer Applications (BCA) final-year project.
The primary objective of this research is to demonstrate how modern software engineering practices can be utilized by student developers to build an efficient, secure, and industry-oriented application capable of automating resume evaluation and improving candidate employability. The study addresses several challenges prevalent in the recruitment ecosystem, including manual resume screening, inconsistent candidate evaluation, lack of ATS awareness among job seekers, inefficient skill assessment, and difficulties in matching candidates with relevant job opportunities. To overcome these limitations, the Resume Analyzer introduces an automated scoring mechanism that evaluates resumes based on multiple parameters such as technical skills, projects, work experience, educational qualifications, certifications, and professional profiles including LinkedIn and GitHub.
The system leverages a modern technology stack consisting of Python, Flask, SQLite, HTML, CSS, JavaScript, PyPDF2, and Regular Expression-based text extraction techniques. The research investigates the effectiveness of adopting contemporary software development methodologies, including modular architecture, role-based user management, session-based authentication, responsive user interface design, and automated resume parsing strategies.
Through iterative development and user-centric design principles, the platform was successfully engineered to provide accurate ATS scoring, intelligent resume analysis, candidate profile evaluation, and job matching capabilities within a centralized environment.
Furthermore, this paper examines system architecture decisions, database design considerations, security implementation techniques, and performance optimization strategies that contribute to the reliability and scalability of the platform. The findings demonstrate that integrating automated resume analysis with ATS-based evaluation significantly enhances recruitment efficiency while providing valuable feedback to job seekers. The proposed framework serves as a practical reference model for future recruitment technologies, placement management systems, and intelligent career guidance platforms, while also bridging the gap between academic learning and real-world software development practices.

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