How OCR turns pixels back into text
Optical character recognition is the art of undoing rasterisation: an image of a page is just a grid of coloured pixels, and OCR's job is to find the shapes in that grid that are letters, decide which letters they are, and stitch them back into words and lines. Modern engines do this in stages — first detecting regions that contain text, then segmenting lines and words, then running each word image through a recognition model trained on millions of samples of printed type.
This tool runs Tesseract, the most battle-tested open-source OCR engine, compiled to WebAssembly so it executes inside your browser at near-native speed. The engine and the language model are downloaded once from a CDN and cached; your image is decoded, analysed, and recognised entirely on your own machine. That has a privacy consequence worth spelling out: the photo of your contract, ID, or receipt is never transmitted anywhere when you use basic OCR.
Recognition output is richer than a wall of text. For every word the engine reports a confidence score and a bounding box — the exact pixel rectangle it read the word from. The side panel uses both: words under 75% confidence get a red underline so you know what to proofread, and hovering any word highlights its rectangle on the original image so verification is instant.
What people use image-to-text for
The most common case is the humble screenshot: an error message, a code snippet from a video, a quote from a slide, an address in a chat picture — content that is text to a human but pixels to your clipboard. OCR turns it back into something you can paste, search, and edit. It beats retyping every time, and the confidence highlighting tells you whether a quick skim is enough.
The second big family is paperwork. Receipts for expense reports, invoices for bookkeeping, business cards into contacts, printed forms into spreadsheets, book pages into study notes. For one-off documents the free in-browser path is ideal; for batches of scans where structure matters — line items in a table, headers versus body text — the Pro HD pass reconstructs the layout instead of flattening everything into one stream of words.
Accessibility and archiving round it out. Text extracted from images can be read aloud by screen readers, translated, and indexed by search. A searchable PDF — the original image with an invisible, perfectly aligned text layer — is the de-facto standard for digitised records precisely because it preserves the visual document while making every word findable.
Getting the most accurate results
OCR accuracy is mostly decided before the engine ever runs, by the quality of the input. Resolution matters first: characters need to be roughly 20 pixels tall or more to recognise reliably, so prefer the original screenshot over a re-compressed copy, and photograph documents close enough that the text fills the frame. Sharpness matters second — a slightly blurry photo that looks fine to your eye can halve recognition accuracy.
Geometry and lighting come next. Shoot from directly above so lines stay straight, avoid shadows falling across the page, and favour even, diffuse light over a harsh flash that blows out part of the text. Crop away busy backgrounds when you can: the less non-text content the engine has to consider, the fewer false detections you get.
Finally, tell the engine what it is reading. Recognition models are per-language — the English model has no idea what an umlaut or a Cyrillic letter is, so French text read with English settings comes out mangled. Pick the right language from the picker (the data downloads automatically), and watch the red underlines: a handful of flagged words is normal, but if half the result is underlined, the input likely needs a better photo or a different language.
Basic OCR vs HD OCR — an honest comparison
Free basic OCR runs Tesseract's LSTM recogniser in your browser. On clean printed text — screenshots, PDFs rendered to images, flatbed scans — it is genuinely excellent, and its privacy story is unbeatable because nothing is uploaded. Its weaknesses are structure and noise: output is plain text in reading order, tables collapse into word soup, and accuracy degrades on skewed photos, low contrast, and unusual fonts.
HD OCR, part of the Pro plan, runs PaddleOCR on our servers — a modern deep-learning detection-plus-recognition pipeline that consistently tops open benchmarks. The practical differences: it reconstructs layout (paragraphs, tables, and headers come back as structure, not just a stream of words), it detects languages automatically and handles mixed-language documents, it is markedly more robust to skew, noise, and odd typography, and it can emit a searchable PDF with the text layer aligned to the original image.
A good rule of thumb: start with the free pass — it is instant and private, and for most images it is all you need. Reach for HD when the document is structured (tables, forms, multi-column layouts), when the photo quality is rough, or when you need the searchable-PDF output for archiving. Each HD run costs one AI credit on the Pro plan.
Privacy, language support, and the wider toolkit
Because basic recognition is fully client-side, this tool is safe for sensitive material: contracts, IDs, medical paperwork, financial statements. The engine and language files are fetched from a CDN and cached by your browser; the image itself stays in memory on your device and is gone when you close the tab. Only the optional Pro HD pass transmits the image — over TLS, processed, and never used for anything but your job.
Language coverage spans the world's major scripts: Latin-alphabet languages from English to Vietnamese, Cyrillic for Russian and Ukrainian, Greek, right-to-left Arabic and Hebrew, the Indic scripts of Hindi and Bengali, Thai, and the CJK family — Japanese, Korean, and both Simplified and Traditional Chinese. Each model downloads only when you pick it, so the first run in a new language takes a few extra seconds and is instant afterwards.
OCR also slots into a longer pipeline. Got a PDF? Render its pages with our PDF-to-image tool, then OCR the result. Going the other way, our image-to-PDF tool binds images into a document, and the compressor shrinks scans before you share them. Together they cover the full journey from paper to pixels to editable, searchable text — with the private, in-browser path as the default at every step.