A scanned PDF is a photograph of a document. It looks like text, but there is no actual text data inside — just a matrix of pixels. Search does not work. Copy and paste fails. PDF readers cannot index it. Text extraction tools return empty results. The fix is Optical Character Recognition (OCR): a process that reads the pixel pattern on each page, identifies characters, and reconstructs the text. LuraPDF embeds Tesseract — the most widely used open-source OCR engine in the world, maintained by Google — as a WebAssembly binary that runs directly inside your browser tab. The engine downloads once and then processes your document entirely on your device. No file upload, no server API call, no remote processing. Your scanned tax return, signed contract, patient record, or historical document never leaves your machine.
Privacy is the defining reason to choose browser-based OCR over server-based alternatives. Scanned documents are disproportionately sensitive: people scan tax returns, medical records, legal filings, bank statements, and identity documents. Uploading those to a cloud OCR API — even one with a privacy policy — means the file travels over the internet, sits on a server, passes through processing pipelines, and is stored temporarily in ways outside your control. LuraPDF's architecture eliminates that risk structurally. The Tesseract WASM binary runs in a sandboxed Web Worker inside your browser. The only data that moves is the OCR'd text layer being written back into a PDF in memory — all local. The output is a searchable PDF where the original page image is preserved exactly and an invisible text layer is added underneath, perfectly aligned to match the character positions Tesseract identified.
Scanned PDFs accumulate in every industry. OCR unlocks them. Here are the workflows where local, private OCR is the only acceptable approach.
Executed contracts, deeds, and court filings are often scanned and filed as image PDFs. OCR them locally to make every clause searchable in the document management system without uploading confidential legal documents to a cloud service.
Libraries, archives, and genealogical researchers scan historical newspapers, letters, ledgers, and manuscripts. Tesseract supports historical Latin fonts and non-standard character sets. Run OCR to make century-old documents searchable without transmitting fragile historical materials to a third-party server.
Pre-digital academic papers, conference proceedings, and journal scans are not searchable by default. OCR them to enable Ctrl+F search, annotation, citation extraction, and feeding into reference management tools.
Scanned expense receipts and invoices contain amounts, dates, and vendor names locked in image pixels. OCR converts them to searchable, selectable text — enabling copy-paste into accounting software or downstream data extraction.
Legacy patient records, referral letters, and clinical forms arrive as scans. Protected health information is too sensitive to upload to a cloud OCR API. Run OCR locally to make records searchable while keeping PHI on the practice's device.
Document intelligence pipelines that extract entities, classify content, or summarize PDF documents require a text layer to work. OCR scanned PDFs locally with Tesseract WASM to generate searchable PDFs or raw text files that feed NLP models without exposing document data to external APIs.
Tesseract WASM in the browser combines research-grade OCR accuracy with the privacy guarantee of local processing. Here is what that combination delivers.
When you upload a scanned PDF, pdf.js renders each page to an HTML canvas at a target resolution of 200 DPI (configurable to 300 DPI for Accuracy mode). The canvas image data is transferred via a SharedArrayBuffer to a Tesseract.js Web Worker running the selected LSTM language models. Tesseract performs layout analysis to segment the page into text regions, then applies the LSTM neural network to each region to recognize character sequences. The output is a list of words with their recognized Unicode character sequences and bounding box coordinates — the pixel position on the page where each word appears.
Once Tesseract finishes processing a page, pdf-lib uses the recognized text and bounding boxes to draw an invisible text layer on the corresponding PDF page. Each word is placed at its detected coordinates using `page.drawText()` with a font size calculated from the bounding box height and a text color of `rgb(0, 0, 0)` at opacity zero — invisible visually, but present in the PDF's text content stream. Modern PDF viewers use this text stream for search, selection, and copy operations. The result is a PDF that looks exactly like the original scan but responds to Ctrl+F, supports text selection, and can be indexed by document management systems and search engines.
| Feature | LuraPDF | Server-based OCR (ilovepdf, Smallpdf) | Adobe Acrobat |
|---|---|---|---|
| Privacy | Browser-only — file never uploaded | Scanned document uploaded to remote server | Local, but $$$ subscription required |
| Language support | 100+ languages via Tesseract WASM | Varies — typically fewer languages | Acrobat: many, but limited multilingual |
| Cost | Free forever, no page quota | Freemium — page limit or paywall | $$$ Acrobat subscription |
| Signup required | None — open page and run OCR | Account required for multi-page docs | Adobe ID + subscription required |
Scan quality is the single largest factor in OCR accuracy. These tips help you get the best result from Tesseract WASM.
Select the correct language — Tesseract accuracy drops significantly when the wrong language model is applied. If you are unsure, select multiple likely languages and Tesseract will vote between them.
Higher scan resolution produces better OCR — 300 DPI scans achieve significantly higher accuracy than 150 DPI or lower, especially for small-font text and non-Latin scripts.
Crop and rotate before OCR — use the LuraPDF Crop PDF and Rotate PDF tools to align pages upright and remove margins before running OCR. Skewed or upside-down pages degrade recognition quality.
For large multi-page documents on mobile, switch to a desktop browser — Tesseract WASM is processor-intensive and mobile devices are slower. Tablet or desktop Chrome or Firefox gives the best throughput.
After OCR, use PDF to Text to extract the full recognized text as a plain file for pasting into a word processor, translation tool, or data pipeline.
For multilingual documents with mixed scripts — a legal contract with English and Arabic sections, or a paper with English text and Chinese figures — select all relevant languages before running OCR rather than processing sections separately.
Drop your scanned PDF into the upload area above, select the document language, and let Tesseract WASM make every page searchable — entirely in your browser. No upload, no server, no account, no watermark, no page quota. Your scanned tax documents, legal filings, medical records, and archival materials stay on your device from the moment you select them to the moment the searchable PDF lands in your downloads folder. After OCR, extract the full text with PDF to Text, crop and rotate scans with the Crop PDF and Rotate PDF tools, or annotate the newly searchable pages with the Annotate PDF tool.