Automated Resume Parser Using Natural Language Processing - Shaikh, Shahabaz; Mallebhari, Md Mustafa; Siddavatam, Shakila (International Journal of Research in Interdisciplinary Studies, 2025)
Nowadays Recruiters invest a significant amount of time manually reviewing job seekers CV to identify key details such as skills, experience, and employment status etc. This process is quite time-consuming but also prone to human error. In this paper, we are going to propose an automated resume parsing system leveraging Natural Language Processing (NLP) to extract some specific details of the candidate's in efficiently. Our approach consists of Named Entity Recognition (NER), and deep learning models such as BERT to enhance the accuracy of information which is to be extracted. Unlike traditional rule-based parsers that rely on rigid keyword matching, our model demonstrates adaptability to different resume formats and text variations.