OCR de PDF — PDF escaneado a texto

No — the complete OCR pipeline runs inside your browser tab without transmitting a single byte to any remote server. The tool uses two WebAssembly libraries that execute locally: MuPDF, an open-source PDF engine compiled to WebAssembly, rasterises each page of the PDF into a bitmap image entirely within the browser's sandboxed memory. Tesseract.js, a WebAssembly port of the widely used Tesseract OCR engine, then receives those bitmap images and performs character recognition against its trained language model, also in the same browser sandbox. The recognised text is written into the browser's DOM and offered as a plain-text download — all without leaving your device. You can verify this concretely by opening your browser's DevTools Network panel before dropping the PDF: during the entire OCR job, the only outbound requests you will observe are the one-time downloads of the Tesseract engine (~3 MB), the MuPDF library, and the language training data file for whichever language you selected. After those assets are fetched and cached in the browser's storage, the Network tab shows complete silence for the remainder of the job, including all page rendering, all OCR recognition, and the final text output. This architecture matters for sensitive document types. Scanned contracts awaiting signature, medical examination reports, passports and identity documents, immigration paperwork, and internal corporate filings all contain information that most organisations and individuals have strong reasons not to transmit to third-party servers. Because every computation happens inside your browser, WikiPlus receives no copy of your document, no copy of the recognised text, and no metadata about the file you processed.

The tool ships with nine languages: English, Spanish, German, French, Italian, Dutch, Polish, Portuguese, and Russian. These cover the major Latin-script and Cyrillic-script languages used in business, legal, and academic contexts across Europe and the Americas. Each language uses Tesseract's LSTM-based neural network model trained on large corpora of printed text in that language, which provides substantially better accuracy on modern typefaces than older pattern-matching approaches. Accuracy depends primarily on scan quality, font clarity, and page layout. A clean 300 DPI black-and-white scan of a standard book page or typed letter in any of the supported languages typically achieves 97 to 99 percent character accuracy on Latin scripts under good conditions. Reducing scan resolution below 200 DPI, using heavily compressed JPEG scans with visible artefacts, capturing documents at an angle with a phone camera, or processing pages with low contrast between ink and paper all reduce accuracy into the 85 to 93 percent range. Handwritten text is not reliably recognised; Tesseract's models are trained exclusively on printed typefaces and perform poorly on cursive or informal handwriting regardless of language. Multi-column layouts are processed column by column, with the tool concatenating columns into a single text stream in reading order. Tables survive OCR as space-aligned plain text but lose their formal row-and-column structure. If your document is in a language not listed, the English model provides partial coverage for European languages that share cognates and proper nouns with English, though accuracy for language-specific characters will be reduced. For Arabic, Chinese, Japanese, Korean, and other non-supported scripts, a dedicated offline tool or commercial OCR service with appropriate training data is necessary.

La primera página de una sesión nueva siempre es la más lenta porque el navegador tiene que descargar el motor Tesseract (~3 MB) y los datos de entrenamiento del idioma (10–50 MB según el idioma). Esa descarga única suele tardar entre 10 y 30 segundos en una conexión doméstica de banda ancha y después los datos quedan en la IndexedDB del navegador, por lo que cualquier OCR posterior en el mismo idioma arranca en menos de un segundo. El reconocimiento en sí funciona a unos 2–5 segundos por página en un portátil moderno para una página A4 estándar. Un PDF de 10 páginas se termina en unos 30–45 segundos contando el calentamiento. Un escaneo de 100 páginas puede llevar de 4 a 8 minutos. El tiempo escala de forma aproximadamente lineal con las páginas. Los dispositivos móviles son entre 2 y 3 veces más lentos, así que los documentos largos conviene procesarlos en portátil. El navegador no se congela mientras se ejecuta el OCR; la herramienta usa un Web Worker para que la página principal siga respondiendo y puedas cambiar de pestaña durante el proceso.

Garbled OCR output almost always traces to one of three root causes, each with a clear remedy. The first and most common cause is a language mismatch: Tesseract's neural network models are optimised for the phoneme and character frequency patterns of specific languages, and running a German document through the English model, for example, produces confident but incorrect substitutions for umlauted characters such as ä, ö, and ü. Check the language selector, choose the language that matches the document's script and language, and re-run. The fix takes seconds. The second cause is poor scan quality. Tesseract achieves its highest accuracy on scans captured at 300 DPI or above in black-and-white or greyscale mode with good contrast. JPEG compression artefacts, skewed page angles above roughly 10 degrees, shadows from book bindings, bleed-through from reverse-side printing, and heavy background textures all degrade character recognition. Re-scanning at 300 DPI with a flatbed scanner rather than a phone camera typically restores accuracy to the 97-plus percent range. The third cause is that the PDF already contains an embedded digital text layer and does not actually require OCR. Some PDFs appear to be scans — perhaps because they were exported from image editing software — but already have a searchable text layer beneath the visual image. In those cases the OCR engine is doing redundant and less accurate work compared to simply extracting the existing layer. Use the PDF to Text tool first; if it returns readable content, you have a digital text PDF and OCR is unnecessary. For historical documents with unusual typefaces, heavy ornamentation, or degraded inks, specialist services with historical-text training data such as Transkribus produce better results than a general-purpose OCR engine.