AI Resume Parsing: Enhancing Recruitment Efficiency through Machine Learning

Abstract — The research paper provides a solution to current recruitment management's high volume of resumes. Resume screening operations in businesses have become time-consuming and uneven due to rising job application volumes. The research considers artificial intelligence systems using machine learning and natural language processing to parse and analyze candidate information automatically. The three components of our resume analysis system are named entity recognition meaning text classification with semantic analysis to improve precision in qualification searching. The proposed technique demonstrates 94% precision in information extraction with different resume formats and uses 75% less screening time than human evaluation methods. The research contributes to smart recruitment system development through its scalable technique that maximizes hiring efficiency and reduces human bias in evaluation of the candidate.