AI-Powered Resume Parser & Automated ATS Candidate Ranking System
Automates corporate recruitment candidate filtering by parsing PDF/DOCX resumes, extracting competencies using spaCy, and matching job descriptions.
Problem Statement
Recruitment teams receive hundreds of resumes per vacancy, requiring dozens of manual screening hours and often introducing subjective human bias.
Proposed Solution
An automated Application Tracking System (ATS) pipeline that extracts contact details, education, work experience, and technical skills from diverse resume formats, computing a ranked semantic relevance score against target Job Descriptions (JD).
Key System Features
System Architecture & Pipeline
End-to-end data transformation pipeline from input capture to output visualization.
Project Modules Breakdown
Resume Text Ingestion & Parsing
Module 1Extracts raw text streams while retaining structural headers using PDFMiner and python-docx.
Named Entity Recognition (NER)
Module 2Trained spaCy language pipeline recognizing domain-specific technical skills and certifications.
Semantic Match Engine
Module 3Vectorizes candidate skills and job descriptions using BERT embeddings, calculating cosine relevance.
HR Management Portal
Module 4Visual portal displaying ranked candidates, skill match breakdown, and interview shortlist triggers.
Complete Technology Stack
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Package Deliverables
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