HR Automata: An Intelligent Automated Recruitment and Candidate Evaluation System

Recruitment is a critical function in organizations, yet many hiring processes still rely on manual resume screening and repetitive administrative tasks. These traditional methods are time-consuming, error-prone, and inefficient when handling large numbers of applications. This paper presents HR Automata, an intelligent recruitment automation system designed to streamline candidate evaluation and hiring workflows. The proposed system integrates Natural Language Processing (NLP), semantic similarity analysis, plagiarism detection, automated email communication, and AI-based interview question generation into a unified platform. Candidate resumes are automatically parsed to extract relevant information such as skills, education, and professional experience. The system compares extracted information with job descriptions using semantic matching techniques to generate candidate relevance scores. Additionally, the platform verifies document originality through TF-IDF based similarity detection and supports automated reference verification. Experimental evaluation demonstrates that HR Automata significantly reduces manual HR effort and accelerates recruitment processes while maintaining consistent candidate evaluation. The system provides an efficient solution for modern recruitment environments.