Resume Text Extraction Explained: How ATS Converts Your File to Data
Before the ATS can score your resume, it must first extract the raw text from your file. This text extraction step is where many resumes fail silently—the ATS converts your carefully formatted document into plain text, and in the process, critical information can be lost, jumbled, or misinterpreted. Understanding text extraction helps you avoid these invisible failures.
How Text Extraction Works for DOCX Files
DOCX files (Microsoft Word format) are essentially ZIP archives containing XML files. The text content is stored in a file called document.xml within the archive. The ATS parser opens this XML file and reads the text content from paragraph and run elements.
Because DOCX files have a clear document structure, text extraction is generally reliable. The parser can identify headings, paragraphs, lists, and tables from the XML tags. It reads content in the order defined by the document's XML structure, which usually matches the visual layout.
However, DOCX files with complex formatting—such as floating text boxes, SmartArt, or content nested inside shapes—can cause extraction issues. These elements are stored separately from the main document flow and may be read out of order or skipped entirely.
How Text Extraction Works for PDF Files
PDF text extraction is significantly more complex than DOCX. PDFs store text as individual characters positioned at specific coordinates on the page. Unlike a word processor document, there's no inherent concept of paragraphs, lines, or reading order in a PDF.
The parser must reconstruct the text flow by analyzing character positions—grouping nearby characters into words, words into lines, and lines into paragraphs. For simple, single-column PDFs, this reconstruction is usually accurate. For multi-column layouts or complex designs, the parser may combine text from different columns or read sections out of order.
Additionally, some PDFs don't contain extractable text at all. PDFs created by scanning paper documents are essentially images with no text layer. PDFs exported from design tools like Canva or InDesign may embed text as vector graphics rather than searchable text.
| PDF Type | Text Extractable? | Parsing Reliability |
|---|---|---|
| Created from Word/Docs | Yes | High (85-95%) |
| Created from LaTeX | Yes | High (85-95%) |
| Created from Canva | Partially | Low-Medium (50-70%) |
| Created from InDesign | Partially | Low-Medium (50-70%) |
| Scanned document | No (without OCR) | Very Low (30-50%) |
| Created from HTML | Yes | Medium-High (75-90%) |
Elements That Break Text Extraction
Several common resume elements cause text extraction failures. Images and graphics are completely ignored—any text rendered as part of an image is invisible to the parser. This includes logos, icons, skill bars, and infographic-style elements.
Headers and footers in both DOCX and PDF files are stored separately from the main body content. Many parsers skip these entirely, which is problematic if you put your name or contact information in the header.
Watermarks, background images, and decorative elements add noise to the text extraction process. While they usually don't prevent extraction, they can introduce unexpected characters or spacing issues that affect parsing accuracy.
- Images and graphics: completely invisible to text extraction
- Headers/footers: often skipped by parsers
- Text boxes: may be extracted out of order
- Tables: cell content may merge across columns
- Watermarks: can introduce noise characters
- Embedded fonts: may cause character encoding issues
Testing Your Resume's Text Extraction
You can test how well your resume's text will extract by performing a simple copy-paste test. Open your resume, select all text (Ctrl+A), copy it (Ctrl+C), and paste it into a plain text editor like Notepad. The result approximates what the ATS parser sees.
If the pasted text is complete, in order, and readable, your resume will likely parse well. If text is missing, out of order, or garbled, you have extraction issues that need to be fixed.
For PDF files specifically, you can use free online PDF-to-text converters to see what text the parser can extract. Compare the output against your original to identify any missing or misplaced content.
Pro Tips
Always perform the copy-paste test before submitting: Ctrl+A → Ctrl+C → paste into Notepad to see what the ATS sees
Save your resume as DOCX from Microsoft Word or Google Docs for the most reliable text extraction
If you must use PDF, create it by saving/exporting from a word processor, not from a design tool
Never place critical information in headers, footers, text boxes, or images
Use standard character encoding and avoid special Unicode characters that may not extract properly
Common Mistakes to Avoid
Creating a PDF resume from Canva, which often embeds text as images or vector graphics rather than extractable text
Using fancy Unicode bullet characters (★, ▪, ➤) that may not extract correctly across all systems
Placing your name and contact info in the document header instead of the main body
Not testing your resume's text extraction before submitting to employers