AI resume parser online: see your CV as structured data

What gets extracted

The parser combines text extraction with AI field recognition, so messy layouts still map onto clean fields.

• Contact block: name, email, phone, city, links
• Experience: employer, job title, start and end dates, bullets
• Education: institution, degree, field, graduation date
• Skills, languages with levels, certifications

Scanned and legacy files

If a file has no text layer, OCR runs as a fallback so photographed or scanned resumes still produce fields. Old .doc files and Word documents with tables are handled too — exactly the formats that most often break in recruiting software.

From parsed data to a clean resume

Parsed output loads straight into the editor. Correct anything the parser misread, pick a template, and export a version whose structure a parser can no longer get wrong.

How the parse score is calculated

Every extracted field is checked for presence, plausibility and position, then rolled into one percentage so you can see at a glance what an ATS would keep.

• Contact recovery (25%) — name, email, phone and city found in readable text, not in a header or image
• Experience structure (30%) — each role has an employer, a title and a start/end date the parser can read
• Education and dates (15%) — consistent formats such as 03/2021 – 08/2024
• Skills and keywords (20%) — listed as text, not as bars, icons or graphics
• Layout safety (10%) — single column, no tables or text boxes breaking reading order

Who uses a resume parser

The same extraction step happens on both sides of a hiring process.

• Job seekers checking what survives before they apply
• Career changers converting an old DOC or scanned CV into an editable draft
• Recruiters and small agencies turning inbound PDFs into structured candidate data
• Anyone applying in Germany who needs a DIN 5008 layout built from an existing CV

Full sample letters

Checklists before you send

Frequently asked questions

Is my uploaded resume stored?

Parsing happens for your session and the result belongs to your draft. You can delete the draft at any time, and your account data can be removed entirely.

Why did the parser miss a job title?

Usually because the title sits inside a graphic, a text box or a column that linearises out of order. That is precisely what a real ATS would also miss.

Which formats are supported?

PDF, DOCX, legacy DOC and images via OCR.

How accurate is the parser?

On single-column, text-based PDFs it recovers contact details and role structure almost completely. Accuracy drops on multi-column designs and scans — which is exactly the signal you want before applying.

What is a good parse score?

Above 85% means an ATS will read your resume the way you intended. Below 60% usually points to a layout problem rather than a content problem.

Can I edit the parsed data afterwards?

Yes. Every field opens in the editor, so you can fix a misread employer or date and export a clean, parser-safe version straight away.

Is the parser free?

Yes, uploading a CV and viewing the structured result costs nothing and needs no sign-up for the check itself.

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