Intelligent Automated System for Parsing and Ranking Resumes

Resume parsing is a method used to extract key information from resumes, allowing for further actions such as candidate selection and ranking.  In traditional recruitment processes, companies often handle thousands of resumes manually or require applicants to follow a pre-defined template.  However, the evolving recruitment environment calls for more advanced technological solutions and efficient resume analysis methods.  Although various basic techniques can analyze structured documents, they are inadequate for processing unstructured formats such as PDF, DOC, and DOCX.  The current methods for resume parsing primarily rely on techniques such as BERT, Natural Language Processing (NLP), keyword-based models, and named entity recognition (NER) models.  In response to this, the proposed system introduces a new approach that uses Computer Vision through YOLOv8 and Large Language Models (LLMs) for enhanced performance and broader API integration.  YOLOv8 is used for resume segmentation, while Tesseract OCR extracts relevant information in variable text format.  The extracted data are then processed by two LLMs using the Gemini and OpenAI APIs, which compute similarity scores and rank candidates according to specific criteria.