Resume Parse

Your resume gets read twice. The first reader is software.

Before a recruiter opens your resume, something else has already opened it: a parser. It takes your carefully laid-out document, strips away the design, and tries to reconstruct a structured record — name, contact details, employer, job title, start date, end date, skills. Everything downstream depends on how well that reconstruction goes.

Most “ATS-friendly resume” advice stops at use a simple layout and never explains why. This site explains why. Not with rejection-rate statistics — those numbers get repeated everywhere and stand up nowhere — but with the actual mechanics: how text extraction works, what reading order is, why a two-column layout can interleave your job titles with your dates, and what happens to a skills section that lives inside a table.

What you’ll find here

• What the machine sees. A walk through the extraction step, from file bytes to a list of fields.
• The layout constructs that break it. Columns, text boxes, tables, headers, footers, and graphics — what each one does to your text.
• File formats, honestly. What PDF, DOCX, and plain text each store, and which one to send when.
• Fields, not keywords. How dates and titles get recognised, and why an unusual heading can hide an entire job.
• How to test it yourself. Simple checks that show you exactly what a parser would extract.

One thing we will never publish: tricks for showing the software something the human can’t see. Hidden white text and keyword stuffing are easy to catch, they get applications thrown out by the person who actually decides, and they are the opposite of what this site is for. The goal is an accurate machine reading of a true document.

Read the blog →

A parser isn’t judging you. It’s trying to read you. Make that easy and it will hand a complete, accurate picture to the human on the other side.