See What an ATS Reads From Your Resume

A resume parser reads the text layer inside a PDF and can identify common resume sections such as contact information, work experience, education, skills, projects, and certifications. This tool shows the extracted text, word and character counts, detected headings, reading order, and contact details and links found in the text. It does not score your qualifications or reproduce an employer's hiring system.

Text can go missing or appear in a different order because of scanned PDFs, columns, tables, text boxes, headers, footers, icons, damaged files, or the way the PDF was exported. The parser attempts OCR for scanned pages, but recognition may fail or produce errors; unreadable files can require a fresh text-based export. Compare the result with your original PDF, make the needed change, export a new PDF, and test that file again.

Yes — a two-column resume can parse cleanly, and some parsers detect columns well. The honest caveat is that you cannot know which parser sits behind your next job posting: column detection varies between systems, and when it fails, sidebar text splices into your experience sentences. If you prefer a two-column design, keep one wide, clearly separated sidebar, keep contact details in plain text instead of icons, and keep nothing critical in the narrow column. Then test the final export here — the extracted stream shows exactly how the columns interleaved, and the reading-order check flags it. If the stream reads wrong, switch to the single-column version of your template.

No. Employers use different applicant tracking systems, parser versions, settings, and hiring processes. This tool shows what ResuPulse can read from your resume and helps you find possible text, heading, and reading-order problems. It cannot reproduce every employer's result.

The extractor reads native PDF text and uses rule-based heading detection. For pages identified as scanned, it attempts OCR; this does not verify that recognized content is accurate. Running an extraction does not require an account. If you are signed in, the structured report is also saved to your history for review and sharing; guest uploads are stored temporarily for the report page.

Yes. Extractions are free, and you do not need an account to run one. There is no credit system and no per-day charge — the parser reads your PDF's text layer directly with fixed rules, so a test is cheap to run. If you sign in, each report is also saved to your history so you can revisit or share it later.

You can test PDF files up to 5 MB. Native text-based exports are preferable. The parser attempts OCR on scanned pages, but image quality and processing availability can limit the recovered text. Word files can be exported to PDF and then tested.

Yes — not because one program is always better, but because each one builds the PDF differently. Word, Google Docs, Canva, and design tools each write the text layer in their own way: some split lines into many small text objects, some place text inside groups or images, and some reorder content behind the scenes. The same resume can extract differently depending on which program produced the PDF. The practical rule is simple: finish your file in one program, export the final PDF, and test that exact export. If you move the file to another program and export again, treat it as a new file and test it again.

Processing time depends on file structure, page count, connection, and whether scanned pages need OCR. Native text extraction is generally simpler than image recognition. If processing fails, try a fresh text-based PDF export within the 5 MB limit.

Yes. The tool works with text-based PDFs whether you call the document a resume or a CV. The same checks apply either way: the extracted text stream, the reading order, the detected section headings, and the contact details and links are all read the same. CVs that use different section names, such as Publications or References, go through the same heading check — uncommon names may be extracted with the text but not recognized as standard sections, and the report shows that explicitly.

These are details the parser can identify inside the extracted text, such as email addresses, phone numbers, and links. If your contact information only appears inside an image or icon, it may not appear in the extracted result.

Usually yes — but check, because you cannot tell from looking at the rendered page. Accented characters like é, ñ, or ü are normal text, and most modern exporters store them in the text layer without problems. The risky cases are symbols from icon or decorative fonts, heavy ligatures, and text that looks fine visually but was flattened or embedded oddly during export. The parser test shows the exact characters it received, so scroll to your name, degree, or certifications in the extracted stream and confirm they survived the export. If a character came out wrong, retype it in the source file, export again, and retest — and if a symbol only exists as an image, replace it with plain text.

Test the exact PDF you plan to submit — not an earlier draft, and not the original Word or Google Docs file. Every export produces a new PDF: fonts can embed differently, columns can shift, and the same document saved from a different program can come out with a different text order. If you make a layout change or export again, run the test on that new file before you send it anywhere.

Not necessarily. A very small text result can point to a scanned or image-only PDF with no useful text layer. If text was extracted but no headings were found, your section names may simply be different from the standard headings used by the parser.

Start with the issue shown in the report. For layout problems, a simpler single-column structure can make the reading order easier to follow. For heading problems, use clear section names such as Experience, Education, Skills, or Projects. Export the updated PDF and test that new file again.