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Towards smarter hiring: resume parsing and ranking with YOLOv5 and DistilBERT

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Abstract

In the contemporary landscape of recruitment, the Applicant Tracking System (ATS) plays a pivotal role in automating the screening and shortlisting of candidates. However, the prevailing ATS encounters challenges such as imprecise data extraction, erroneous keyword selection, and a lack of standardized criteria for comparison. As a result, many deserving applicants are turned away, highlighting the necessity for a more complex and human-centered strategy. In response to these limitations, our research introduces an innovative Resume Parsing and Ranking solution. Leveraging advanced natural language processing techniques and machine learning algorithms, our system provides a customized experience for the automated screening process. The naive methods underscore the distinct advantages of our innovative approach, emphasizing the need to build a robust and accurate model for Resume Parsing and Ranking. Notably, it addresses discrepancies arising from diverse resume structures, ensuring a standardized and equitable evaluation of all applicants. The main contribution of our work lies in the development of a state-of-the-art Resume Parser that enhances efficiency, reduces bias, and optimizes candidate selection outcomes. Our proposed method integrates cutting-edge technologies to refine the existing ATS process, offering a tailored and precise approach to resume evaluation. The primary problem addressed is the lack of precision and standardization in thecurrent ATS, leading to suboptimal candidate shortlisting. Our solution tackles this by introducing a comprehensive framework that mitigates the impact of varied resume structures, thereby promoting fair and consistent candidate assessment. Through empirical validation, our obtained results showcase an accuracy of 96.2% in resume parsing, thereby significantly improving the efficiency of the candidate selection process.

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Authors and Affiliations

  1. School of Engineering and Technology, Dr. Vishwanath Karad MIT-WPU, S.No.124, Paud Road, Kothrud, Pune, 411038, Maharashtra, India

    Shakti Kinger

  2. K. J. Somaiya Institute of Technology, Somaiya Ayurvihar Complex Eastern Express Highway Near Everard Nagar, Sion East, Mumbai, 400022, Maharashtra, India

    Divija Kinger, Shivam Thakkar & Devashish Bhake

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  1. Shakti Kinger
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Kinger, S., Kinger, D., Thakkar, S. et al. Towards smarter hiring: resume parsing and ranking with YOLOv5 and DistilBERT. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18778-9

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