Extract Text from Images (OCR)
Convert images to editable text instantly using advanced OCR technology.
How to extract text from an image — 3 steps
- 1
Add the image
Drop in a photo, screenshot, or scan containing text. The recognition engine runs in your browser, so the picture is never uploaded — which matters when the thing you are reading is a document rather than a sign.
- 2
Run the recognition
The image is analysed and the characters it contains are identified. Larger or noisier images take longer, because the work is being done by your own processor rather than a datacentre.
- 3
Copy or download the text
Review what came back, fix anything the engine misread, and copy it out. Always proofread — OCR is very good, not perfect.
What decides whether OCR works well
Optical character recognition is pattern matching against shapes, so the quality of the input governs the quality of the output far more than any setting does. Resolution is the first factor: text needs to be reasonably large in the image for the engine to resolve individual letters. A photo of a page taken from across a desk will produce noticeably worse results than the same page filling the frame, even though both are 'readable' to you.
Contrast and evenness of lighting come next. Crisp black text on white paper is the ideal case. A photograph with a shadow falling across half the page, a glossy page reflecting a window, or faded grey text on cream stock all give the engine less signal to work with. If you can retake the shot with flat, even light and the page filling the frame, that single change usually helps more than anything else.
Then there is the text itself. Clean printed type in a common font is recognised very reliably. Handwriting is substantially harder and often unusable. Decorative or condensed fonts, text over a photograph, tables and multi-column layouts, and pages photographed at an angle all reduce accuracy — skew in particular, because letters that are rotated or keystoned no longer match the shapes the engine expects.
Where OCR genuinely helps, and where to be careful
The everyday uses are the ones worth knowing: pulling a paragraph out of a screenshot instead of retyping it, capturing a serial number or Wi-Fi password from a photo of a label, lifting text from a slide or an infographic, getting a quotation out of a photographed book page, and turning a scanned form into something you can search.
Two habits are worth forming. First, always proofread the output. OCR mistakes cluster around characters that look alike — 0 and O, 1 and l and I, 5 and S, rn and m — and those are exactly the errors that slip past a quick glance in a reference number or a postcode, where they matter most. Second, expect layout to be lost. OCR returns characters, not design; columns, tables, and indentation usually come back as a linear run of text that needs restructuring.
If the source is a scanned PDF rather than a single image, a dedicated PDF OCR pass is the better route, because it can process every page in one go and write the recognised text back into the document so it becomes searchable rather than just extracted.
Your files never leave your device
Recognition runs entirely in your browser, which is the whole point for this tool — the images people run OCR on are typically documents, invoices, letters, and forms, and those are precisely the things that should not be uploaded to a stranger's server.
Most online PDF sites upload your document to their servers, process it there, and keep a copy for minutes, hours, or longer. PilotPDF works differently: the page downloads a small processing engine into your browser, and your file is opened and edited in your device's memory using WebAssembly. Nothing is transmitted, so there is nothing for a server to store, leak, or scan. You can even disconnect from the internet after the page loads and the tool keeps working.
Read more about how this works in our privacy-by-design explainer.