AI-based Multimodal Resume Ranking Web Application for Large Scale Job Recruitment

This paper presents a resume-ranking web application that improves recruitment through advanced deep-learning techniques. The system uses the YOLOv9 model fine-tuned with our newly created custom dataset for segment detection on resumes of various structures, EasyOCR for text recognition, mBERT fine-tuned for text classification, and GLiNER for named entity recognition with regular expressions. These models and techniques efficiently extract, categorize, and match resume information with job descriptions. We created a custom dataset for our object detection training, and while we trained three models, YOLOv9 achieved the highest performance with a score of 0.84 mAP. Our hybrid matching approach provides highly accurate and relevant resume rankings using the embedding model, gte-large-en-v1.5, and cosine similarity for semantic matching with dense vectors with extracted keywords and BM25 for keyword relevance. The web application allows HR professionals to upload resumes seamlessly, define job descriptions, and view ranked results, providing a tailored solution to specific recruitment needs. Although we faced challenges such as text extraction accuracy and zero-shot NER limitations, our system demonstrated a solid overall performance. This paper demonstrates the potential of state-of-the-art deep learning models to enhance recruitment processes and provides a valuable tool for HR professionals to identify the most suitable candidates efficiently.