Written by the founder of BreezeConvert — a creative director working across set design, game 3D, Unreal/VFX and AI content pipelines. More about the author.
A screenshot of a document, a photo of a page, a scanned contract — they look like text, but to a computer they're just pixels. You can't select a sentence, search for a word, or paste them anywhere useful. OCR (optical character recognition) is the step that reads those pixels and reconstructs the actual characters, giving you text you can copy, edit, and feed into anything else. Here's how to get a good result and where the technology genuinely can't help.
How OCR actually works
An OCR engine looks at the shapes in an image, matches them against the letterforms of a language you specify, and outputs characters. The "language you specify" part matters more than people realise: telling the engine the text is Korean, or Japanese, or English changes which character set it expects and dramatically affects accuracy. For a document with two languages mixed together, a combined mode (like Korean + English) usually beats either one alone.
Getting the most accurate result
OCR quality is decided mostly by the input, so a little care up front beats fiddling with settings afterward:
Resolution and contrast. A crisp, high-contrast scan — dark text on a clean white background, roughly 300 DPI — reads far better than a dim phone photo. If a result comes back garbled, rescanning at higher quality fixes it more often than anything else.
Straighten the page. Skewed or curled pages confuse the engine. Line the page up square before capturing.
Pick the right language. This is the single biggest lever, especially for non-Latin scripts.
Know what won't work. Handwriting, decorative fonts, busy backgrounds, and very low-resolution images all reduce accuracy sharply. General OCR is built for printed text; it is not a handwriting reader.
Scanned PDFs are a special case
A "scanned PDF" is just images wrapped in a PDF — there's no real text inside, which is why copy-paste and search don't work on it. OCR is exactly what turns it into something usable. A good tool renders each page to an image and reads them in order, so a multi-page scan comes out as continuous text. (This is also the honest reason a plain "PDF to text" or "PDF to Markdown" converter returns nothing on a scan: there's no text to extract until OCR creates it.)
Doing it without uploading the document
The pages people most need to OCR are often the sensitive ones — a contract, an ID, a medical form, a page from a private notebook. Uploading those to an unknown OCR website to read them is a real privacy trade. It isn't necessary: our image-to-text (OCR) tool runs a WebAssembly build of the Tesseract engine entirely in your browser, so the file never leaves your device. You can confirm it on the verify-it-yourself page or by running it offline.
After OCR: clean it up for AI
Once you have the text, it usually needs a light tidy — and if your goal is to feed it to Claude or ChatGPT, the Markdown tool will count the tokens, strip junk characters, and let you apply a prompt or split a long document into chunks. OCR plus that clean-up turns a stack of scans into something an assistant can actually work with.
Related guides: Preparing documents for AI · YouTube transcript to text · PDF drawing to DXF.