OpenResumeParser
OpenResumeParser is a free, MIT-licensed project for extracting structured information from resumes and comparing skills with a job description.
What OpenResumeParser is
OpenResumeParser is a free, MIT-licensed project for extracting structured information from resumes and comparing skills with a job description. It accepts PDF and TXT files and can identify names, email addresses, phone numbers, skills, experience, and education. Its skill-matching output includes a score, skills that match, and skills that are missing. Users can work through a command-line tool or a FastAPI REST API; batch processing is supported. The curated skills database covers 120+ technical skills and can be customized in extractor.py. The project includes Docker and docker-compose setup, and its README describes it as type-safe and notes its use of Pydantic models. It is listed for API, self-hosted, and Windows use, with API access available. The MIT License allows personal and commercial use. It does not handle scanned PDFs or OCR. Experience and education extraction is heuristic-based, the skills database is curated rather than ML-based, and date extraction relies on regular expressions. Questions or bug reports can be raised through a GitHub issue.
Good
- Accepts PDF and TXT resumes
- Provides a command-line tool and REST API
- Matches skills and reports missing skills
- Curated database covers 120+ technical skills
- MIT licensed for personal and commercial use
Watch out
- Does not support scanned PDFs or OCR
- Experience and education extraction is heuristic-based
- Skills database is curated rather than ML-based
- Date extraction uses regular expressions
Who it suits
It suits developers or teams seeking a self-hosted resume parsing and skills-matching project with a command-line tool or API. Its listed formats and extraction limits matter for workflows involving scanned documents or precise date parsing.
Verdict
OpenResumeParser provides resume field extraction and job-skill matching through command-line and API interfaces. Its limitations around scanned files and heuristic extraction are important to account for when choosing it.
Plans and prices
From OpenResumeParser's own pricing page, checked 7 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| MIT-licensed project | Free | MIT license · personal and commercial use |
The facts
Each one read from OpenResumeParser's own pages, with the date it was checked.
What it does
- Deployment
- The project includes a Dockerfile and docker-compose setup.checked 7 Oct 2026
- Extracted fields
- It extracts names, email addresses, phone numbers, skills, experience, and education.checked 7 Oct 2026
- Input formats
- It parses resumes in PDF and TXT formats.checked 7 Oct 2026
- Interfaces
- The project provides a command-line tool and a FastAPI REST API.checked 7 Oct 2026
- License
- The project is offered under the MIT License for personal and commercial use.checked 7 Oct 2026
- Notable limitations
- It does not support scanned PDFs or OCR, and experience and education extraction is heuristic-based.checked 7 Oct 2026
- Other limitations
- The skills database is curated rather than ML-based, and date extraction uses regular expressions.checked 7 Oct 2026
- Purpose
- The project extracts structured data from resumes and matches skills against job descriptions.checked 7 Oct 2026
- Security and typing
- The README describes the project as type-safe and says it uses Pydantic models.checked 7 Oct 2026
- Skill matching
- It compares resume skills with a job description and returns a match score, matched skills, and missing skills.checked 7 Oct 2026
- Skill taxonomy
- The README says its curated skills database supports 120+ technical skills and can be customized in extractor.py.checked 7 Oct 2026
- Support
- The README directs users to open a GitHub issue for questions or bugs.checked 7 Oct 2026
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