pyresparser

Why we included this project

Trying to extract clean structured data from free-form resumes usually means wrestling with inconsistent formatting. pyresparser offers a simple path: it reads PDFs and DOCX files and returns a dictionary with the essentials, such as name, email, phone number, skills, degree, employers, and an estimate of years of experience. Under the hood it uses spaCy and nltk for entity recognition and text cleanup, so the extraction is based on actual NLP rather than just pattern matching. You can call ResumeParser on a single file, or use the CLI to process a whole directory and get the extracted data back; being plain Python with documented dependencies, it's easy to drop into an existing backend, and you can customize the skills list or add your own regex rules if the defaults don't match your use case. Just keep in mind its scope: it's built for extracting the obvious fields from a resume, not for full career guidance, so run it against your own documents before you rely on it at scale.