How to extract text from an image
- Drop a picture on the dropzone, click to browse, or paste a screenshot straight from your clipboard, anything with printed or typed words works.
- Watch the status line while the engine loads and scans. The very first run fetches the OCR engine once (see below); after that recognition starts almost immediately.
- Read the result in the Extracted text box. It's a normal, editable text area, so fix any stray characters right there.
- Click Copy text to put the cleaned-up words on your clipboard, ready to paste into a doc, email or search box.
OCR that keeps your image on your device
Optical character recognition reads the shapes in an image and works out which letters and numbers they represent, turning a flat picture of text into characters you can select, search and edit. The usual online converters do this by sending your file to a server, which is exactly what you don't want when the image is a payslip, a passport page, a signed contract or a screenshot of a private chat. This tool runs the open-source Tesseract engine inside your browser, compiled to WebAssembly, so the picture is analysed on your own machine and never leaves it, only the engine's code and a language file are fetched, and only the first time.
OCR is genuinely useful for retyping a receipt into a spreadsheet, lifting a quote out of a photographed book page, grabbing an error message from a screenshot so you can search it, or pulling contact details off a business card. It is not magic, though, and honesty matters here: accuracy is a direct function of image quality. Crisp, high-contrast black text on a plain white background reads almost perfectly; a low-resolution photo taken at an angle in dim light will produce mistakes.
How OCR works and where it struggles
Under the hood the engine cleans up the image, finds lines and word boundaries, isolates each character, and matches its shape against a trained model of the alphabet, then uses a dictionary to resolve ambiguous guesses. That pipeline is strong on regular printed fonts and weak wherever the shapes get irregular. Handwriting is unreliable, cursive especially so. Decorative, condensed or very small fonts trip it up, as do busy backgrounds, watermarks, low contrast, glare, motion blur and text set at an angle. You can improve your odds a lot before scanning: crop tightly to just the text, straighten the image so lines are horizontal, and make sure the resolution is high enough that individual letters are clearly formed rather than a few fuzzy pixels tall. If a page came out dark or skewed, re-shooting it flat under even light will beat any amount of post-processing. Treat the output as a fast first draft, always proofread it against the original, particularly for digits, punctuation and look-alikes such as O/0 and l/1/I.
Frequently asked questions
How does image-to-text work without uploading?
The Tesseract OCR engine is compiled to WebAssembly and runs inside your browser. Your image is decoded and recognised on your own device, so it's never transmitted to a server. Only the engine and its English language data are downloaded, once, from a CDN.
How accurate is the recognition?
It depends almost entirely on the source image. Sharp, high-contrast printed text on a clean background is transcribed very accurately, while blurry, low-resolution, angled or low-contrast images produce errors. OCR is never guaranteed to be perfect, so always proofread the result, digits and punctuation especially.
What kinds of images work best?
Screenshots of on-screen text, flat scans of documents, book and magazine pages, printed receipts, signs and slides. Aim for horizontal lines, even lighting and text that is at least a couple of dozen pixels tall. Handwriting and heavily stylised display fonts are the weakest cases.
Which languages are supported?
This page recognises English. Tesseract itself handles many scripts, but each language ships as its own data file that has to be downloaded, so this tool loads only English to keep the first run fast. English works fine for text that mixes in the odd accented word.
Why is the first run slower than the rest?
The first image triggers a one-time download of the OCR engine and the English model, a few megabytes in total. The browser caches those files, so every image after the first skips the download and is recognised far more quickly.
Can I fix the text after scanning?
Yes. The extracted text lands in an editable box, so you can correct any misread characters before copying. For longer cleanups, paste it into the case converter to fix stray capitals or the word counter to check length.