🧠 CV Screening & Candidate Filtering System

An AI-powered resume screening platform that automates the first stage of recruitment — extracting, evaluating, and ranking candidate CVs with speed and precision.
Designed for HR teams to handle high-volume hiring efficiently, consistently, and at scale.

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📋 Overview

The CV Screening and Candidate Filtering System enables recruiters to:

• 📄 Upload job descriptions and batches of candidate CVs (PDF).
• 🧠 Automatically extract key candidate information (skills, contact details, etc.).
• ⚖️ Compare candidate profiles against role requirements using a deterministic scoring model.
• 🟢🔸🔴 Generate a quantitative match score and preliminary Accept / Review / Reject decision.

This removes repetitive manual screening and ensures standardized, fair, and explainable candidate evaluations.

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🎯 Project Goals

• 🚀 Automate resume screening
  Parse and structure diverse CV formats to reduce manual work.

• ⚖️ Standardize candidate evaluation
  Use a transparent scoring model for fairness and consistency.

• 📈 Scale with volume
  Handle 50+ CVs in a single batch reliably.

• 🧭 Support HR decision-making
  Provide clear breakdowns of matched vs. missing skills.

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🏗️ Features

📝 Job Management

• Create new job roles by providing a title and full job description.
• System auto-extracts Mandatory and Preferred skills.
• View or delete active jobs and their candidate records.

📥 Candidate Input

• Upload multiple CVs (PDF) for a selected job.
• Instant processing and extraction.

🧾 Data Extraction

• Extracts:
  • ✅ Full Name
  • 📧 Email Address
  • 📞 Phone Number
  • 🌐 Professional Links (LinkedIn, GitHub, Portfolio)
  • 🛠 Extracted Skills
• Uses PDF.js, RegEx, and NLP heuristics.

🧮 Candidate Evaluation

• Custom scoring methodology compares candidate skills with job requirements using weighted metrics.
• Generates a match score and preliminary decision.

📊 Results Management

• Real-time updates when new CVs are added.
• Filter and view candidates by job, score, and status with clear visualization.

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🧰 Tech Stack

Component	Technology	Rationale
🖼 Frontend	HTML5, Tailwind CSS, TypeScript (ES6+)	Lightweight, responsive UI
📄 Data Extraction	PDF.js, RegEx, heuristic parsing	Fast, client-side OCR/NLP simulation
🗃 Database	SQL (SQLite / PostgreSQL)	Structured storage for analytics
🔐 Authentication	Supbase (Custom/Anonymous)	Secure, user-scoped access

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🧭 Database ERD Diagram

The following Entity–Relationship Diagram (ERD) illustrates the structure and relationships between core tables in the CV Screening & Candidate Filtering System.

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🧱 Database Schema

Jobs Table

Stores job criteria and definitions.

Field	Type	Description
job_id (PK)	INTEGER	Unique Job ID
user_id	TEXT	Owner/HR identifier
title	TEXT	Job Title
mandatory_skills	JSON	Required skills list
preferred_skills	JSON	Nice-to-have skills list
created_at	TIMESTAMP	Creation time

Candidates Table

Stores candidate data and evaluation results.

Field	Type	Description
candidate_id (PK)	INTEGER	Unique Candidate ID
job_id (FK)	INTEGER	Associated Job ID
name	TEXT	Candidate Name
email	TEXT	Contact Email
phone	TEXT	Contact Number
extracted_skills	JSON	Parsed skills
match_score	REAL	Computed score (0-100)
decision	TEXT	Accept / Review / Reject
submitted_at	TIMESTAMP	CV submission time

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🧪 Testing & Validation

Test Area	Criteria for Success	Purpose
📑 Extraction	≥ 95% accuracy for name, email, phone, links	Robust RegEx/NLP
🧮 Scoring Accuracy	100% match to weighted criteria	Verify business logic
✅ Decision Logic	Perfect threshold adherence	HR requirements
⚡ System Stability	Batch of 50+ CVs processed without failure	Scalability & error handling

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🧭 Architecture Highlights

• 🧩 Modular Design — OCR, Scoring Engine, and Database are loosely coupled for easy maintenance.
• 📊 Scalable & Stable — Handles high loads without downtime.
• 🌐 Responsive UI — Simple, modern single-file web app.
• 🧠 Transparent Evaluation — Explainable scoring for better HR trust.

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📌 Future Enhancements

• 🤖 Integrate actual NLP models for semantic skill matching.
• 📨 Email notifications to candidates.
• 📊 Dashboard analytics for HR insights.
• ☁️ Cloud deployment & CI/CD pipeline.

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🚀 Getting Started

# 1️⃣ Clone the repo
git clone https://github.com/yourusername/cv-screening-system.git

# 2️⃣ Install dependencies (if any)
cd cv-screening-system
# e.g., npm install

# 3️⃣ Run locally
# Open index.html in your browser or use a local dev server