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OpenResumeParser
What it is
OpenResumeParser extracts structured information from PDF and TXT resumes and compares resume skills with a job description. It can identify names, email addresses, phone numbers, skills, experience, and education. For a skill comparison, it returns a match score, matched skills, and missing skills. Users can run it from a command line or use its FastAPI REST API; batch processing is supported. The curated skills database covers 120+ technical skills and can be customized in extractor.py. Deployment files include Docker and docker-compose, and the README describes the project as type-safe and using Pydantic models. It is available as a self-hosted project and lists Windows among its platforms. OpenResumeParser is free under the MIT License for personal and commercial use. It does not process scanned PDFs or provide OCR. Experience and education extraction is heuristic-based, the skills database is curated rather than ML-based, and date extraction uses regular expressions. The project directs users to open a GitHub issue with questions or bugs.
For and against
For
- Accepts PDF and TXT resumes
- Returns match scores and missing skills
- Command-line and FastAPI REST API interfaces
- Curated database covers 120+ technical skills
- Free for personal and commercial use
Against
- 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
The verdict
OpenResumeParser combines resume field extraction with job-skill matching and offers both CLI and API access. Its lack of OCR and heuristic extraction make it important to review results for the intended workflow.
Who it suits
It suits people who want to extract resume details and compare skills with job descriptions using a command-line tool or API. It is not suited to workflows requiring OCR for scanned PDFs.
Plans and prices
MIT-licensed project
MIT license · personal and commercial use
Read 7 Oct 2026 · github.com
On the record
- Deployment
- The project includes a Dockerfile and docker-compose setup.github.com · read 7 Oct 2026
- Extracted fields
- It extracts names, email addresses, phone numbers, skills, experience, and education.github.com · read 7 Oct 2026
- Input formats
- It parses resumes in PDF and TXT formats.github.com · read 7 Oct 2026
- Interfaces
- The project provides a command-line tool and a FastAPI REST API.github.com · read 7 Oct 2026
- License
- The project is offered under the MIT License for personal and commercial use.github.com · read 7 Oct 2026
- Notable limitations
- It does not support scanned PDFs or OCR, and experience and education extraction is heuristic-based.github.com · read 7 Oct 2026
- Other limitations
- The skills database is curated rather than ML-based, and date extraction uses regular expressions.github.com · read 7 Oct 2026
- Purpose
- The project extracts structured data from resumes and matches skills against job descriptions.github.com · read 7 Oct 2026
- Security and typing
- The README describes the project as type-safe and says it uses Pydantic models.github.com · read 7 Oct 2026
- Skill matching
- It compares resume skills with a job description and returns a match score, matched skills, and missing skills.github.com · read 7 Oct 2026
- Skill taxonomy
- The README says its curated skills database supports 120+ technical skills and can be customized in extractor.py.github.com · read 7 Oct 2026
- Support
- The README directs users to open a GitHub issue for questions or bugs.github.com · read 7 Oct 2026
