PDF para Texto

No — this tool extracts the digital text layer that is already encoded inside the PDF's content streams, and that layer does not exist in scanned documents. A scanned PDF is, at its core, a series of raster bitmap images wrapped in a PDF container. There are no character codes, no font references, and no Unicode mappings for the engine to read; the pixels representing letters are visually meaningful to a human eye but are opaque binary data to a text extractor. If you run this tool on a scan-only PDF, it will return an empty result or very sparse output from headers and form fields that happened to be generated digitally. To get text from a scanned document you need optical character recognition, which analyses pixel patterns to infer characters. For that, use our dedicated PDF OCR tool, which runs Tesseract.js entirely in your browser, supports nine languages, and handles the full pipeline from page rendering to text output without uploading anything. Alternatively, Google Drive's OCR feature, Adobe Acrobat's Scan & OCR function, or the open-source Tesseract command-line tool all add a searchable text layer directly into the PDF. Once an OCR pass has embedded the text layer, run the processed PDF through this extractor for clean, accurate, structured output. For any PDF that was created digitally — Word exports, Google Docs downloads, LaTeX-compiled papers, InDesign exports, or any modern publishing workflow — the text layer is always present and this tool extracts it byte-accurately, preserving the original Unicode characters and reading order.

Yes, with fidelity proportional to how well-structured the source PDF is. The PDF format stores text as a sequence of positioned glyph-drawing commands rather than as tagged semantic content, so reconstructing paragraph boundaries requires heuristics. The extraction engine analyses the vertical gaps between lines, the horizontal indentation of each text run, and the spacing between character clusters to infer where paragraphs begin and end, where list items break, and where headings stand apart from body text. PDFs produced by word processors — Microsoft Word, Google Docs, LibreOffice Writer, Apple Pages — embed consistent spacing metrics that make reconstruction highly reliable; paragraphs appear separated by blank lines, bullet characters are preserved as Unicode symbols, and numbered lists maintain their prefix patterns. Academic papers typeset in LaTeX produce similarly clean output because LaTeX applies rigid typographic rules. Multi-column layouts, common in journal articles and magazines, are detected by analysing the horizontal distribution of text runs; columns are read in their natural left-to-right order. Tables are extracted as tab-separated rows, which preserves the relational structure for copying into a spreadsheet. Highly decorative PDFs from graphic design applications, where text is placed as arbitrary floating objects without a consistent layout grid, produce less reliable paragraph breaks. The output encoding is plain UTF-8 with no markup, making it directly usable in text editors, translation tools, large language model prompts, or any application that accepts plain text input. The entire extraction runs in your browser — no page content reaches any server.

Text extraction speed is primarily governed by the number of pages and the complexity of each page's font and character-mapping data, not raw file size. A typical 100-page PDF composed mostly of body text extracts in under two seconds on a modern laptop running Chrome or Firefox. A 500-page academic textbook typically completes in 8 to 15 seconds. The bottleneck is font resolution and ToUnicode table parsing — the engine must map each glyph code to its Unicode equivalent for every unique font in the document, which involves reading embedded font programs and character mapping tables from the PDF's cross-reference structure. Pages that contain only images with no text layer are processed almost instantly because the extractor skips their content streams after confirming they carry no glyph data. PDFs with many unique embedded fonts, such as multilingual documents or design files with dozens of custom typefaces, take longer than single-language text PDFs. There is no page-count limit imposed by the tool; the practical ceiling is your browser's available RAM, which ranges from roughly 2 GB on mid-range phones to 8 GB or more on desktop browsers with generous memory budgets. Chrome and Firefox both manage their tab memory aggressively, so very large PDFs above 500 MB may require a desktop browser rather than a mobile one. The processing runs entirely in your browser via a WebAssembly PDF engine, meaning there is no upload wait, no server queue, and no network latency added to the extraction time. Subsequent extractions of the same file are faster because the browser caches the library.

Yes, provided the source PDF was created with standard Unicode-compliant font encoding. The PDF specification requires fonts to include a ToUnicode CMap that maps each glyph identifier to one or more Unicode code points; when that map is present and correct, the extractor delivers character-accurate text for any script the font covers. Accented Latin characters used in Spanish (ñ, á, é), French (à, ê, œ), Portuguese (ã, ç), German (ä, ö, ü), and Polish (ł, ź, ę) all pass through correctly from well-formed PDFs. Cyrillic alphabets used in Russian, Bulgarian, Ukrainian, and Serbian are handled with equal accuracy. Greek, Arabic (including right-to-left directionality), Hebrew, Devanagari, Chinese CJK ideographs, Japanese kana and kanji, Korean Hangul, and Thai all extract accurately when the PDF's font encoding is correctly specified. The one known failure mode is PDFs that use a custom proprietary font with a missing, incomplete, or deliberately obfuscated ToUnicode map — a technique sometimes used by DRM systems to prevent text extraction. In those files, glyphs may appear as Unicode replacement characters, question marks, or incorrect letters; this is a limitation of the PDF format itself rather than the extraction tool. The fix is to obtain or recreate the document using a standard font with a complete Unicode mapping. For any PDF generated by a major office suite or publishing application, correct Unicode output is the default and expected behaviour. Extraction runs locally in your browser with no data transmitted externally.