Resume Parser API

A fast, lightweight resume parser built with Python and FastAPI. It extracts structured data from raw resume text using regex patterns and heuristics — no ML models, no external AI APIs.

Built this as a backend microservice that takes plain text resumes and returns clean JSON with contact info, education, work experience, skills, and more.

What It Extracts

• Name — from the top of the resume using multiple detection strategies
• Emails, Phone Numbers, Websites — regex-based extraction from the header
• Date of Birth — if labeled in the resume
• Addresses — scored against known Indian and US cities, states, and PIN/ZIP codes
• Summary / Objective — detected by section headers
• Education — degree, institution, year, GPA, field of study
• Work Experience — company, job title, date range, description
• Skills — section-based parsing + full-text scan against 200+ known skills
• Certifications — listed certifications
• Projects — project name, technologies used, description

Every field comes with a confidence score (0.0 to 1.0) so the consumer knows how reliable each extraction is.

Tech Stack

• Python 3.11+
• FastAPI — async REST API framework
• Uvicorn — ASGI server
• Pydantic — request/response validation
• Regex + Heuristics — all parsing is rule-based, zero ML dependencies

Project Structure

ResumeParser/
├── main.py                        # FastAPI app, endpoints, server startup
├── pyproject.toml                 # Dependencies and project config
├── run_server.bat                 # Quick-start script (Windows)
├── test_parser.py                 # Manual smoke test with sample resume
├── app/
│   ├── core/
│   │   ├── config.py              # Server settings (host, port, log level)
│   │   ├── parser_engine.py       # Main orchestrator — runs all extractors
│   │   └── section_detector.py    # Splits resume into named sections
│   └── extractors/
│       ├── name_extractor.py      # Candidate name extraction
│       ├── contact_extractor.py   # Emails, phones, URLs, DOB
│       ├── address_extractor.py   # Physical address parsing
│       ├── education_extractor.py # Degree, institution, GPA, year
│       ├── experience_extractor.py# Job title, company, dates, bullets
│       ├── skills_extractor.py    # Skills (section + full-text keyword scan)
│       └── section_extractor.py   # Certifications and projects

How It Works

1. Section Detection — scans the resume line by line and splits it into sections (header, summary, education, experience, skills, etc.) using regex pattern matching on section headers.

2. Extraction Pipeline — the parser engine runs 10 extractors in sequence, each focused on one data type. Every extractor receives only its relevant section text.

3. Confidence Scoring — each extractor returns a confidence score. The engine computes a weighted overall confidence where name, emails, skills, and experience are weighted highest.

4. JSON Response — returns all extracted data, per-field confidence scores, overall confidence, and which sections were detected.

Getting Started

Install Dependencies

pip install -e .

Start the Server

python main.py

Server runs on http://localhost:2700

API Endpoints

Parse Resume

POST /parse-resume

Request body:

{
  "resume_text": "John Doe\njohndoe@gmail.com | +91-9876543210\n\nSummary\nFull Stack Developer with 5 years of experience..."
}

Response:

{
  "success": true,
  "data": {
    "name": "John Doe",
    "emails": ["johndoe@gmail.com"],
    "phoneNumbers": ["+91-9876543210"],
    "websites": [],
    "dateOfBirth": null,
    "addresses": [],
    "summary": "Full Stack Developer with 5 years of experience...",
    "education": [],
    "workExperience": [],
    "skills": ["Python", "FastAPI", "React"],
    "certifications": [],
    "projects": []
  },
  "confidence": {
    "name": 0.88,
    "emails": 0.99,
    "phoneNumbers": 0.95,
    "skills": 0.90
  },
  "overall_confidence": 0.85,
  "sections_detected": ["header", "summary", "skills"]
}

Health Check

GET /health

Returns {"status": "ok", "version": "1.0.0"}

API Docs

FastAPI auto-generates interactive docs at http://localhost:2700/docs

Run the Test

python test_parser.py

Parses a sample resume and prints the full JSON output to verify everything works.