resumeminer 0.1.0

A lightweight Python library for extracting structured information from PDF resumes

ResumeMiner

A lightweight Python library for extracting structured information from PDF resumes.

Features

• PDF text extraction via pypdf
• Email and phone number extraction
• LinkedIn, GitHub, and portfolio URL extraction
• Configurable skill matching against a bundled default list
• Simple API and command-line interface
• Structured dictionary output with raw extracted text

Installation

pip install resumeminer

Requires Python 3.9 or newer.

Development

git clone https://github.com/alixaprodev/resumeminer.git
cd resumeminer
pip install -e ".[dev]"

Quick Start

from resumeminer import ResumeParser

parser = ResumeParser("resume.pdf")
result = parser.parse()

print(result["email"])
print(result["skills"])

from resumeminer import parse_resume

result = parse_resume("resume.pdf")
print(result)

Optional custom skills list:

parser = ResumeParser("resume.pdf", skills=["Python", "Rust", "Go"])
result = parser.parse()

CLI Usage

Parse a resume:

resumeminer parse resume.pdf

Human-readable output (default):

Email:     [e-mail]
Phone:     [phone]
LinkedIn:  https://linkedin.com/in/jane-developer
GitHub:    https://github.com/janedev
Portfolio: https://janedeveloper.dev
Skills:    Python, Django, React, Docker, AWS

Full JSON output (includes raw_text):

resumeminer parse resume.pdf --json

Print version:

resumeminer --version

Output Example

{
    "email": "[e-mail]",
    "phone": "[phone]",
    "links": {
        "linkedin": "https://linkedin.com/in/jane-developer",
        "github": "https://github.com/janedev",
        "portfolio": "https://janedeveloper.dev"
    },
    "skills": ["Python", "Django", "React", "Docker", "AWS"],
    "raw_text": "..."
}

Supported Files

• PDF resumes with extractable text

Scanned or image-only PDFs are not supported in v0.1.0.

Limitations

• Extraction quality depends on PDF structure and formatting
• Regex-based parsing may miss or misread fields on unusual layouts
• Skill detection uses a fixed default list unless a custom list is provided
• Phone and portfolio URL extraction may return imperfect matches on some resumes

Roadmap

• OCR support for scanned resumes
• DOCX and TXT file support
• Name, education, and experience extraction
• Section-based parsing
• Custom skill dictionary file path
• JSON schema output
• Batch parsing CLI

Contributing

Contributions are welcome.

1. Fork github.com/alixaprodev/resumeminer
2. Create a feature branch
3. Add tests for behavior changes
4. Run pytest
5. Open a pull request

Report issues on GitHub Issues.

License

MIT License. See LICENSE.

Author

H. Ali

• GitHub: github.com/alixaprodev
• Email: [e-mail]