PDF-zu-Word-Konverter

This converter is text-first by design, and that design choice has a direct consequence: images, inline figures, charts, complex tables, headers, footers, page numbers, and multi-column layout structures are not reproduced in the output .docx file. The tool extracts each paragraph of text from the PDF's content streams and writes it into the Word document with correct paragraph breaks and hard page boundaries between PDF pages. What you get is the complete, accurately ordered body text of the source document in an editable format — which is precisely what writers, editors, researchers, and translators need. Faithful visual reproduction requires a document model that parses PDF graphics operators, resolves image XObjects, reconstructs table geometry from positioned text runs, and maps all of that into Word's complex XML schema. That is an order-of-magnitude larger engineering problem than text extraction, and browser-based tools that claim pixel-accurate PDF-to-Word fidelity invariably produce mixed results across the enormous variety of real-world PDF structures. If your workflow requires the output to visually match the source — for a client-facing proposal, a designed résumé, or a formatted report — Microsoft Word's built-in File → Export path, Google Docs' File → Download as .docx, or Adobe Acrobat's Export PDF feature all have full access to the original fonts and rendering engine. Use those for layout-critical conversions. For content-critical conversions where the words matter and the formatting will be reapplied in Word anyway, this browser-based tool is fast, completely private, and requires no account or installation. Scanned image-only PDFs produce an empty document because there is no digital text layer to extract; run the PDF OCR tool first to create that layer.

No. The conversion runs entirely inside your browser tab and no part of your PDF or the generated Word document is transmitted to any server. When you drop the file, the browser reads it into an ArrayBuffer using the standard File API, keeping it in the tab's sandboxed memory. MuPDF — compiled to WebAssembly — opens the PDF from that in-memory buffer and extracts the text content of each page. A client-side Office Open XML generator constructs the .docx file structure directly in memory: it creates the required XML parts (word/document.xml for content, [Content_Types].xml for the package manifest, and the relationship files), populates them with the extracted text, and passes the result to JSZip, which compresses the XML parts into a valid .docx ZIP archive. The resulting Blob is offered as a browser download via a temporary object URL. The only network traffic involved in the entire process is the one-time load of the three JavaScript libraries — MuPDF WebAssembly, the OOXML builder, and JSZip — which the browser caches after the first visit, making subsequent conversions effectively offline-capable. You can confirm this by opening your browser's DevTools Network panel before dropping the file: you will see no outbound request carrying PDF bytes at any point. This matters significantly for confidential documents. Draft contracts before signature, salary offer letters before acceptance, internal policy documents before publication, M&A due-diligence materials, and patient records are all document types where most organisations' data-loss-prevention policies prohibit upload to third-party web services. This tool is architecturally incapable of receiving your file.

The generated .docx intentionally uses Word's default Normal paragraph style for every text run — Calibri 11pt (or the equivalent body font in your Word installation's theme), standard line height, one-inch margins, and A4 page dimensions. No bold, italic, underline, heading styles, colours, font changes, or list formatting from the source PDF are applied. This is a deliberate consequence of how the conversion pipeline works. Extracting text from a PDF accurately is straightforward because text content streams are well-structured in the PDF specification. Extracting and mapping visual formatting — determining whether a larger, bolder run of text represents a Heading 1 or just an emphasised sentence; distinguishing a bulleted list from indented body text; mapping PDF font-size hierarchies to Word heading levels — requires heuristic inference that is unreliable across the enormous diversity of real-world PDF layouts. Rather than apply potentially incorrect formatting that you would then need to undo, the tool delivers clean unstyled text that you can format deliberately using your own Word template or your organisation's style guide. Most users find this more efficient: apply a stylesheet once to the output document and the formatting problem is solved globally. For documents where bold headings and list structure are clearly defined — such as technical manuals or policy documents — you will need to re-add those styles manually, which is typically faster than correcting misapplied automated formatting. If preserving the visual appearance of the source PDF is more important than editability, the PDF to SVG tool exports each page as a scalable vector that can be placed as an image inside a Word document.

Large PDFs and password-protected files are both supported with clearly defined practical limits. For file size, the tool handles PDFs up to approximately 300 MB reliably on a modern desktop browser with 8 GB or more of RAM. Beyond that threshold, the browser may exhibit memory pressure symptoms — slow response, tab reload prompts — because the PDF engine must hold the parsed document structure in memory simultaneously with the in-progress .docx being built. Page count is a softer limit: documents of 1,000 pages or more process correctly on desktop browsers but may cause memory pressure on mobile devices, where the operating system enforces tighter per-tab memory ceilings; 100 to 200 pages is the practical mobile limit. For password-protected PDFs, type the credential into the password field displayed below the upload area before clicking Convert. The tool passes the password to MuPDF's native decryption path, which correctly handles both AES-128 and AES-256 user passwords (which block opening) as well as owner passwords that only restrict editing permissions. AES-256 PDFs using PBKDF2-SHA256 key derivation — the current default from Acrobat, Word, and macOS Preview — are fully supported. If the password is incorrect, you receive an explicit wrong-password error message and can retype it without losing the file from the upload area or resetting any other state. Owner-only passwords, which restrict editing but not viewing, are handled transparently without requiring any user action — the same behaviour as a compliant PDF viewer. This tool does not accept unknown passwords and cannot be used to access PDFs whose credentials you do not hold.