About This Tool
Unlock Your PDF Data
PDFs are great for printing, but terrible for editing. Copying text from them often results in broken formatting or weird characters.
Our PDF to Text Converter uses a two-stage approach, but extraction and OCR can still miss or reorder content:
- Native Extraction: Instantly pulls text layers from digital PDFs.
- OCR (Optical Character Recognition): If the entire document yields fewer than 50 native-text characters, English OCR runs on every page.
Local document processing
PDF extraction and OCR run in page memory and are not submitted to a Free Toolset application server. The browser still requests site, model, advertising, and related assets, so avoid highly sensitive documents unless you are comfortable with your device, extensions, and page environment.
Key Features
- Dual-Engine Technology: Combines Native extraction with Tesseract OCR.
- Works on Scans: Extracts text even from flat images or photos of documents.
- Local Processing: Document contents are processed in page memory rather than submitted to a Free Toolset application server; verify consequential extracted text against the original.
- One-Click Copy: Instantly grab all text for Word or Notion.
How this tool works
Methodology reviewed 2026-07-11The extractor uses PDF.js to read text-layer items from every page and joins them with spaces and page markers. If the entire document produces fewer than 50 characters of native text, it renders every page at 1.5× scale and runs English Tesseract OCR instead. Files are limited to 15 MB and 50 pages. Extracted text remains a draft because layout and recognition can be imperfect.
Worked example
A digitally generated invoice may yield selectable text quickly from its embedded layer, while a photographed page requires OCR and can misread columns, handwriting, low contrast, rotation, or similar characters such as O and 0.
How to interpret it: Compare important names, amounts, dates, and clauses with the original document. Reading order, tables, headers, footnotes, and accessibility tags can be lost even when most words appear correct.
Assumptions
- The PDF is readable by PDF.js and does not exceed 15 MB or 50 pages.
- English OCR is appropriate when the whole-document native-text threshold triggers it.
- The browser has enough memory to render the pages.
Limitations
- OCR triggers for every page only when total native text is under 50 characters, so a mixed PDF with enough digital text may leave scanned pages unrecognized.
- Columns, tables, forms, formulas, handwriting, reading order, and scanned artifacts may produce missing or incorrect output.
Sources
Sources explain the standard or planning method; they do not endorse Free Toolset or verify individual results.
Frequently Asked Questions
Does this work on scanned PDFs?
If the entire document produces fewer than 50 characters of native text, the tool runs English OCR on every page. A mixed document with enough digital text may not OCR its scanned pages.
Is there a page limit?
Yes. Files are limited to 15 MB and 50 pages to reduce browser memory and processing failures.
Is the formatting preserved?
The tool extracts raw text with page markers, but item joining can flatten or reorder paragraphs, columns, tables, forms, footnotes, and line breaks.