Extracta Resume Parser API vs OpenResumeParser
Extracta Resume Parser API
extracta.aiOpenResumeParser
github.comSide by side
| Figure | Extracta Resume Parser API | OpenResumeParser |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 1 |
| Spec score · Resume Parsing Software | 8.2 (#12) | 7.4 (#24) |
| Input formats | Resume processing supports PDFs, images, scans, digital documents and text files. | It parses resumes in PDF and TXT formats. |
| Support | The support team handles technical questions through the website contact form or support chat, with tutorials and documentation also available. | The README directs users to open a GitHub issue for questions or bugs. |
Plans
Extracta Resume Parser API
- Free TrialFree
- Custom solutionsPriced on request
- Pay-as-you-goPrice not published
- SubscriptionPrice not published
In their own words
Extracta Resume Parser API
Extracta Resume Parser API has no write-up on Specifiction yet; its figures are above.
OpenResumeParser
OpenResumeParser is a free, MIT-licensed project that extracts structured information from resumes and compares skills with job descriptions. It reads PDF and TXT files and extracts names, email addresses, phone numbers, skills, experience, and education. Its matching output includes a score, matched skills, and missing skills. The project offers a command-line interface and a FastAPI REST API, with API access and batch processing listed. A curated database covers more than 120 technical skills and can be customized in extractor.py. Dockerfile and docker-compose files support deployment, while Pydantic models are used in a type-safe design described by the README. The listed platforms are API, self-hosted, and Windows. The MIT license permits personal and commercial use. Important limits include no scanned-PDF or OCR support, heuristic experience and education extraction, a curated rather than machine-learning skills database, and regular-expression date extraction.