Airparser vs OpenResumeParser

Airparser turns emails, PDFs, and other documents into structured fields you define. Its text and vision language models plus AI OCR handle typed text, scans, and handwriting, with recognition in more than 60 languages. Low-confidence results can wait for human review, correction, and approval before export. Parsed information can be downloaded as Excel, CSV, or JSON, or sent to webhooks and services including Zapier, Make, n8n, Google Sheets, and Airtable. Python post-processing is included in plans, and an AI code assistant can draft and test code from plain-language instructions. Airparser works through the web and an API, with workflow routing and cloud deployment. Each parsed email, document, or PDF page consumes one credit, and unused credits expire at the billing period’s end. A Trial plan is listed at free with 20 credits/month and 30 days’ retention. Paid plans start at $33 a month, billed annually.

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.