Resume Parser : Ai Project

Tech

Python, SQL, GenAI, NLP, NLTK, Scoring

Abstract

The AI-Powered Resume Parser is a multi-user web application that leverages Natural Language Processing (NLP) and Generative AI to intelligently extract, analyze, and score resumes based on job-specific criteria. Unlike traditional keyword-matching systems, this platform uses contextual understanding of resumes to identify real skills and relevance to job roles. Built using Django for the backend and NLTK + GenAI for AI processing, it allows recruiters to upload and evaluate resumes efficiently in a secure, authenticated environment.

Keywords

Resume, Parser, NLP, GenAI, Django, NLTK, AI, Tokenization, Skills Extraction, Matching, Scoring

Project Description

The AI-Powered Resume Parser is designed to automate and enhance resume screening using contextual AI techniques. Unlike basic keyword filters, it evaluates resumes based on actual relevance to job descriptions. Users are assigned roles such as admin, recruiter, or optionally candidate. Resumes are parsed using NLP methods like tokenization, lemmatization, and named entity recognition. GenAI helps in summarizing content and generating recruiter notes. The system scores resumes on a 0–10 scale based on multiple factors including skill alignment, experience, and education. A secure, multi-user dashboard helps recruiters manage, filter, and evaluate candidates effectively.

Project Features

• User Roles: Admin, Recruiter, Candidate
• Resume Upload: PDF/Docx to clean text
• NLP Processing with NLTK, Spacy
• Named Entity Recognition: Skills, Degrees
• Job Role Context Mapping
• Semantic Skill Matching
• Resume Scoring Engine (0–10)
• GenAI Summary Generation
• Recruiter Notes Generator
• Resume Feedback Generator
• Dashboard for Resume Search & Filtering
• Role-based Access Control
• Email Verification
• Secure Resume Storage
• Metadata-Linked Resume Records

Specifications

• Software components: Django, Python, NLTK, Spacy, SQLite, GenAi
• Backend: Django REST Framework
• Frontend: Django Templates or React
• Authentication: Django AllAuth or Built-in
• AI Integration: GPT AI API
• GenAI Functions: Summary, Notes, Feedback
• Hosting: Localhost or free cloud service

Report Contents

• Block Diagram
• Flow Chart
• Components: Name, Images, Details
• Problem Statement
• Abstract
• Introduction
• Methodology
• Challenges and Solutions
• Performance Analysis
• Advantages
• Limitation
• Application
• Future Scope
• Conclusion
• Output Images