AI Powered Multimodal Resume Ranking Web Application for Wide Scale Hiring

This work presents a web-based resume ranking system designed to support large-scale recruitment by automating resume analysis and candidate matching. The system combines modern deep learning models with traditional information re- trieval techniques to extract, organize, and compare resume con- tent against job descriptions. Resume layouts are first segmented, after which textual information is extracted and classified into meaningful sections such as skills, education, and work experi- ence. Relevant entities are then identified to enrich the extracted information. A hybrid matching strategy is applied, combining semantic similarity and keyword relevance to generate accurate and interpretable rankings. The application enables HR profes- sionals to upload resumes, configure job requirements, and view ranked candidates through an intuitive interface. Experimental results indicate that the system performs reliably across diverse resume formats, demonstrating its potential to reduce manual workload and improve the efficiency of recruitment processes.