Extract all text content from a PDF document
There are two kinds of PDFs and they need fundamentally different extraction strategies. Born-digital PDFs, the ones exported from Word, InDesign, LaTeX, or any application that knows it is producing a PDF, store text as actual character codes mapped through font objects. Extracting text from these is a matter of reading the content streams, decoding each character through its font's CMap, and reconstructing reading order from positional metadata. Scanned PDFs contain text only as pixels in embedded images; there are no character codes to extract, so you need OCR (optical character recognition) to recover text, and this tool does not handle that case, use an OCR-specific tool for scanned documents. For born-digital PDFs, the main challenge is reading order reconstruction. The PDF format stores text in whatever order the producing application emitted it, which may not match the visible reading order. A two-column article might have left-column text interleaved with right-column text in the underlying stream because the layout engine drew them page-down rather than column-down. This tool uses positional coordinates and text-block clustering to reconstruct sensible reading order for most documents, including multi-column academic papers and newspaper-style layouts. Typical extraction accuracy on standard documents is 95%+ word-correct; complex scientific notation and aggressive ligatures can introduce character-level errors.
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You need to quote a paragraph from a report but the PDF blocks copy-paste in your viewer.
Input
report.pdf · extract text, pages 2–3
Output
Plain, selectable UTF-8 text preserving paragraph breaks and reading order
The text layer is read directly and reflowed into clean copyable text, which is faster and more accurate than OCR when the PDF actually contains text (not a scan). Reading order is reconstructed so multi-column pages do not come out scrambled.
There are two kinds of PDFs and they need fundamentally different extraction strategies. Born-digital PDFs, the ones exported from Word, InDesign, LaTeX, or any application that knows it is producing a PDF, store text as actual character codes mapped through font objects. Extracting text from these is a matter of reading the content streams, decoding each character through its font's CMap, and reconstructing reading order from positional metadata. Scanned PDFs contain text only as pixels in embedded images; there are no character codes to extract, so you need OCR (optical character recognition) to recover text, and this tool does not handle that case, use an OCR-specific tool for scanned documents. For born-digital PDFs, the main challenge is reading order reconstruction. The PDF format stores text in whatever order the producing application emitted it, which may not match the visible reading order. A two-column article might have left-column text interleaved with right-column text in the underlying stream because the layout engine drew them page-down rather than column-down. This tool uses positional coordinates and text-block clustering to reconstruct sensible reading order for most documents, including multi-column academic papers and newspaper-style layouts. Typical extraction accuracy on standard documents is 95%+ word-correct; complex scientific notation and aggressive ligatures can introduce character-level errors.
Pull text from a PDF report to reuse in a blog post, email, or other document format.
Extract text from forms or invoices to paste into a spreadsheet or database.
Convert a visually formatted PDF into plain text that can be read by screen readers or text-to-speech tools.
Extract text so you can search through it, run word counts, or perform other text analysis.
The extraction pipeline uses PDF.js to parse content streams, walking each text-showing operator (Tj, TJ, '") and accumulating characters with their positioning information. Characters go through the page's font dictionary to map glyph IDs back to Unicode code points, a step that fails when fonts use custom encodings without ToUnicode CMaps, which is why some PDFs extract as strings of unexpected characters (common with PDFs exported from certain older versions of Office that embedded fonts without the needed mapping information).
Reading order reconstruction happens after character extraction. The tool clusters characters into words using x-coordinate proximity and typical word spacing for the detected font size, then clusters words into lines using y-coordinate alignment, then clusters lines into paragraphs using vertical spacing thresholds. For single-column documents this works cleanly. For multi-column layouts, the tool detects column boundaries by looking at horizontal gaps in word density and reads each column top-to-bottom before moving to the next. Headers and footers are identified by repetition across pages and can be excluded from the extracted text if you prefer continuous body-text output.
Two kinds of PDFs consistently give trouble. First, PDFs produced by some OCR pipelines embed a "text layer" over image pages, but the text positioning is approximate to the image, accuracy depends on the OCR quality, not this tool. Second, PDFs that use "invisible" text layers for copy-protection (the rendered text is an image and the underlying text is scrambled or dummy) will extract text but it will not match what you see. For most commercial, academic, and government documents from the last 15 years, extraction is reliable enough to feed directly into downstream processing like summarization, translation, or indexing without manual cleanup.
No. This tool works with PDFs that contain actual text data. Scanned documents stored as images would require OCR, which is not currently supported.
Some PDFs store text in a non-linear order for rendering purposes. The tool does its best to reconstruct reading order, but very complex layouts may not convert perfectly.
The output is plain text, so formatting styles are not preserved. Paragraph breaks and spacing are maintained where possible.
PDF parsing and editing happen in your browser. Documents, and everything inside them, are never uploaded or stored remotely.