How Does an ATS Resume Parser Read Your Resume? The Short Answer
An ATS resume parser converts your uploaded file into plain text, splits that text into sections such as contact details, experience, education and skills, and copies the pieces it recognizes into database/spreadsheet fields like name, job title, employer, dates and etc…. It reads text in a single stream, so columns, tables and text boxes can scramble or lose information so watchout.
The sections below show each stage, what commonly goes wrong, and how to test and format your own resume.
What Does an ATS Resume Parser Do?
A resume parser is the part of an applicant tracking system (ATS) that turns an unstructured/unorganized document into structured, searchable candidate data. When you upload a resume, it fills fields such as name, email, phone, current title, employers, dates, education and skills (PitchNHire).
Parsing only extracts and organizes information. What happens next, such as recruiters searching profiles or software ranking matches, depends on how each employer has set up its system. If you want the full process of building the document itself, start with our guide on how to create a resume.
How Does a Parser Read Your Resume Step by Step?
Most parsers follow the same sequence, although each vendor’s engine differs in the details.
- Receive the file. The system accepts your upload, usually a PDF or Word document(DOCX).
- Extract the text. Text-based PDFs and word-processor files generally expose their text directly, while a scanned PDF may need optical character recognition (OCR) or a text-based version (Recruiterflow). The result is a flat string of text with the visual design removed (DEV Community).
- Split the text into sections. Natural language processing tries to recognize the logical blocks of a resume, such as personal information, work experience, education and skills (Recruiteze). Standard headings make this step easier.
- Identify details inside each section. The parser looks for individual items such as job titles, employers, dates, schools and skills, and may normalize them into a consistent format (Recruiterflow).
- Store the fields. The structured data goes into a candidate profile that recruiters can review and search. No parser is perfect, so most systems let recruiters correct extracted fields (PitchNHire).
What Information Does a Parser Try to Extract?
Parsers look for a standard set of fields, and the exact list depends on the system. The table shows the common targets and the most frequent reasons they come out wrong.
| Resume section | Fields a parser usually targets | Common reason it fails |
|---|---|---|
| Contact details | Name, email, phone, location, profile links | Stored in a header, footer, text box or image |
| Work experience | Job title, employer, start and end dates | Creative headings, abbreviated titles, inconsistent date formats |
| Education | Degree, school, graduation date | Mixed layouts within the section |
| Skills and certifications | Listed skills, tools, licenses | Skill bars, icons or graphics instead of text |
| Other sections | Projects, awards, volunteering, publications | Unusual section names the parser cannot categorize |
Parsers extract skills and certifications reasonably well when they sit in clearly labeled sections and use common terms (PitchNHire).
How Does a Parser Read Columns, Tables and Text Boxes?
A parser reads your document as a stream of text, usually left to right and top to bottom, not as a picture of the page. Employer guidance warns that some systems read a columned layout straight across each line rather than down one column and then the next (Roche). Tables can be read across rows, and text boxes are often ignored or pulled out in the wrong order (DEV Community).
Here is an illustration of how a two-column layout can break. This is a simplified example, not output from a specific ATS.
What you see on the page:
EXPERIENCE SKILLS
Project Manager, Acme Agile, Scrum
2021-2024 Risk tracking
Led a team of 8 Jira