this project, we develop a robust resume parsing system that extracts structured information (such as candidate name, email, skills, experience, education, and project links) from unstructured PDF resumes. The pipeline combines rule-based segmentation, Named Entity Recognition (NER), skill matching, and deep learning models (like BERT and CareerBERT) to produce a clean, structured output suitable for analytics, matching, and automation. The system is designed to scale across diverse resume formats and integrates multiple layers of intelligence—text parsing, semantic embedding, and layout-aware models—for maximum accuracy.
I am seeking your help with latest tools, architecture or anything of that sort to complete the above project.
Thank you.