SmartResume: A Resume Parser Application using Natural Language Processing

Abstract

In today’s dynamic business environment, Information and Communication Technology (ICT) serves as a pivotal force driving innovation, particularly in human resource management. One notable advancement is the emergence of smart resume applications, which are reshaping traditional recruitment practices. These systems utilize advanced technologies such as artificial intelligence (AI), machine learning, and natural language processing (NLP) to facilitate the automation of resume screening and candidate evaluation processes. This paper presents the development of an Automated Resume Parsing designed to reduce the operational load on human resource professionals. By utilizing NLP techniques, the system extracts key candidate information including name, contact details, educational background, and skills from the resume documents. A custom Named Entity Recognition (NER) model is employed to enhance the accuracy and relevance of extracted data. The model was trained using the SpaCy NLP framework to achieve an overall accuracy of 92.4% and an F1-score of 0.90. The extracted data are presented through an interactive web interface for HR personnel, enabling structured and efficient review of applicant information. The results demonstrate that integrating NLP and machine learning in recruitment systems can significantly enhance automation, consistency, and fairness in candidate evaluation processes. The system's effectiveness was demonstrated through successful extraction and structured presentation of applicant information, aligned with organizational hiring criteria. Beyond technical functionality, this study highlights the potential of such human-centered technologies to enhance decision-making, increase recruitment efficiency, and transform the recruiter-applicant interaction by fostering transparency and fairness in the hiring process.