OCR PDF

The component gives rendered page pixels to Tesseract inside the tab; it does not send the selected PDF to the language-data host. Tesseract does fetch the chosen trained-data file from cdn.jsdelivr.net when needed. A browser may cache that asset, but cache retention and offline availability depend on browser settings and storage state, so a previous successful run is not a permanent offline guarantee. On a metered connection, allow roughly 10 to 15 MB for a language model fetch.

Accuracy depends almost entirely on the page you feed it, and this tool will not quote you a number it cannot stand behind. Recognition is strong on clean, high-contrast printed text that sits straight on the page — a laser-printed contract, a typeset report, a crisp flatbed scan. It degrades on fax-quality output, photographs taken at an angle, faint carbon copies, heavily stylised display type, dense tables, text laid over background images, and pages skewed by more than a degree or two. Before you trust an output, use the preview pane: after a run it shows the recognised text for whichever page you are viewing, so you can page through and judge for yourself. When a page comes back wrong, the usual fixes are rescanning it straighter, raising the contrast, or correcting the language selection.

There is no automatic detection — recognition uses whichever language chip you select, and choosing wrongly is the most common cause of nonsense output. Twenty languages are available: English, Spanish, French, German, Italian, Portuguese, Dutch, Polish, Russian, simplified and traditional Chinese, Japanese, Korean, Arabic, Hindi, Turkish, Finnish, Swedish, Norwegian and Danish. Each has its own trained model with its own character set and word statistics, which is why an English model reads a Polish page badly rather than merely imperfectly. Switching the chip and running again reloads the engine with the new model, downloading it once if you have not used that language before. For a document that mixes languages, pick the one that dominates the body text; short foreign passages usually survive, and accented characters outside the selected model are the first thing to be misread.

Searchable PDF mode attempts to add an invisible text layer aligned with the recognised words. Pages with a substantial native text layer are copied rather than OCR-rasterised; mixed pages can keep their content and receive an additional recognised layer. If a page cannot receive a layer, it remains in the download and an amber notice reports it. Search, selection order and copied text depend on recognition quality and positioning, so test the output rather than assuming accessibility or archival conformance. Plain text mode writes a .txt file instead.

Around 300 DPI, in grayscale or bilevel, is the practical target for the scan you start from. Inside the tool every page is re-rendered at 150 DPI before recognition — a deliberate compromise between accuracy and the memory a browser tab can hold — and that render is what the engine sees. Scanning your original far above 300 DPI therefore buys little here, while scanning below roughly 200 DPI costs you: thin strokes merge, small type loses the shapes that distinguish it, and digits and punctuation are usually the first characters to be misread. Other properties of the source matter as much as resolution — even lighting, straight alignment, and genuine contrast between ink and paper. A well-lit 200 DPI scan usually beats a shadowed 600 DPI photograph of the same page by a wide margin.

Handwriting is not something this tool can do reliably. The engine is trained on printed and typeset characters, and the models offered here are the standard printed-text ones; there is no handwriting model in the list. Neat, well-separated block capitals — the kind you get on a form that asked for them — are sometimes recognised well enough to be useful. Ordinary cursive, hurried notes, signatures and margin annotations are not: expect either nonsense or nothing at all for those. If your document mixes a printed form with handwritten answers, running OCR still helps, because the printed part becomes searchable while the handwritten entries simply remain pixels in the page image. For documents that are handwritten throughout, a service built specifically for handwriting recognition is the right tool.