AI Powered Resume Parser for Enhanced Recruitment Process

Abstract

The growing volume of online job applications has made recruitment increasingly challenging, with manual resume screening becoming both time-consuming and prone to human error. This paper addresses the inefficiencies in traditional recruitment by presenting an AIpowered resume parser designed to automate and enhance the hiring process. Leveraging advanced Natural Language Processing and Machine Learning techniques, the system accurately extracts key candidate details including work experience, education, and skills from diverse resume formats and aligns them with recruiter-defined job requirements. It overcomes the limitations of existing parsers by effectively handling varied layouts, recognizing synonyms, and  managing data inconsistencies. The solution also introduces a ranking mechanism to prioritize candidates based on relevance, thus reducing recruiter workload and fostering unbiased decision-making. Experimental results demonstrate the system’s effectiveness in streamlining recruitment and improving matching accuracy, ultimately contributing to a more efficient and equitable hiring process for both employers and applicants..