About Image to Text (OCR)
Tesseract — the recognition engine behind most open-source OCR — compiled to WebAssembly and served from this site rather than a CDN. The engine travels to your image instead of your image travelling to a server, which is also why the tool keeps working with the network switched off once it has loaded.
Drop in a screenshot, a scanned page or a photo of a document and get the text back as characters you can edit, search and paste. The reading is done by Tesseract, which began at Hewlett-Packard in the 1980s, was released as open source in 2005 and spent the following decade under Google's stewardship — it remains the most widely deployed engine of its kind. The picture goes into memory, gets read, and goes no further.
That last point is the whole reason this tool exists in this form. Think about what people actually put through an OCR service: a passport page for a visa application, a payslip for a mortgage broker, a doctor's letter, a contract with a rate in it, a utility bill as proof of address. Uploading any of those to a free web converter means handing a stranger's server a document you would not email to a colleague, and the privacy policies of that category of site are rarely reassuring reading. Here the engine comes to the image rather than the image going to a server.
Every word comes back with a confidence score, and the tool shows you the summary rather than hiding it. This matters because OCR fails quietly: it does not leave a blank where it could not read something, it produces a plausible wrong word. 'rn' becomes 'm', '0' becomes 'O', '1' becomes 'l', and the result reads perfectly until someone acts on a wrong account number. The verdict above the text is deliberately strict — a page counts as clean only when the engine was unsure about nothing at all, because as soon as there is a doubtful word, telling you to look at it beats telling you the page is fine.
Accuracy is mostly decided before the image reaches the engine, and a few habits change the outcome more than any setting. Aim for around 300 DPI: a scan at 150 loses the fine strokes that distinguish similar letters, while going far above 600 adds paper texture rather than detail. Keep the page square to the frame — Tesseract corrects small skews and struggles past about ten degrees. Photograph documents flat with even light rather than at an angle under a desk lamp, because a shadow gradient across a page defeats the thresholding step that separates ink from paper. And crop to the text you want before reading, which both speeds up the pass and stops the engine trying to make words out of a letterhead graphic.
The layout setting is the one control worth understanding. Tesseract analyses page structure before reading, deciding what is a column, what is a heading and what order to read blocks in — and that analysis is where results most often go wrong. Whole page suits a normal document. Single block is for one paragraph with no other layout. Column of text handles receipts and narrow invoices, where the default can interleave two columns into nonsense. Single line is for a caption or one field of a form. Scattered text turns off layout analysis for signs, screenshots and anything where the text has no reading order at all. If a result comes back scrambled rather than misspelled, the layout mode is almost always the fix, not the image.
One honest limitation: this reads printed text, not handwriting. Handwriting recognition is a different problem, needing different models, and Tesseract's accuracy on cursive is poor enough that offering it would be a disservice. English only for now, too — each additional language is another model to download, and shipping one that works well beats shipping ten that half work.