ποΈ Image to Text (OCR)
Extract text from images with in-browser OCR. Private by design.
How to Extract Text from an Image
Upload an image containing text, choose the language, and click Extract Text. The OCR engine loads (first run takes a few seconds), analyzes the image, and fills the text box with what it recognized. Copy or download the result, and always proofread important documents.
Upload a clear image of printed text: a screenshot, scan, photo of a sign, or receipt.
Select the text language. The right language model greatly improves accuracy.
Keep contrast boost on unless your image is already clean black on white.
Click Extract Text, wait for processing, then copy or download.
How OCR Works
Optical character recognition turns pictures of text into actual text in stages. First the image is preprocessed: converted to grayscale, thresholded to pure black and white, and cleaned of speckles. Then the engine segments the page into blocks, lines, and words by analyzing whitespace gaps. Each word is split into character candidates, and a classifier trained on millions of examples scores what each shape most likely is. Finally, language dictionaries and grammar models correct the raw guesses: if the classifier is torn between "rn" and "m", the dictionary knows which forms a real word. Tesseract, the engine used here, is the most widely deployed open-source OCR system in the world, originally developed by HP and now maintained by Google contributors. Running it as WebAssembly in your browser delivers the same core engine with zero server round-trips, which is why this tool can promise genuine privacy.
Getting Accurate Results
Feed the engine the cleanest input you can. Screenshots at native resolution beat photos of screens, which add moirΓ© and glare. If photographing paper, shoot straight-on in even light and fill the frame with the text. The contrast boost option stretches the tonal range before recognition, rescuing gray-on-white text and faded receipts. For multi-column layouts, expect reading order quirks: OCR reads in detected blocks that may not match your eye path. Tables and forms often need manual cleanup. And remember the golden rule of OCR: it is a first draft, not a transcript. Proofread anything that matters.
Frequently Asked Questions
How does the OCR text extraction work?
The tool uses the Tesseract OCR engine, compiled to WebAssembly and running entirely in your browser. It analyzes your image, detects text regions, recognizes individual characters using trained language models, and reconstructs words and lines. English is built in; you can add over 100 other languages from the dropdown.
What kinds of images give the best OCR results?
Clean, high-contrast text works best: screenshots, scanned documents, photographed signs, and receipts. Straight, horizontal text at a decent size (at least 20 pixels tall) recognizes most accurately. Skewed photos, handwriting, decorative fonts, and busy backgrounds lower accuracy. Preprocessing like the contrast boost option can help marginal images.
Can it recognize handwriting?
Not reliably. Tesseract is trained primarily on printed text. Neat, consistent handwriting sometimes yields partial results, but cursive and stylized writing mostly fails. For handwriting, dedicated AI services perform far better; this tool is designed for printed and screen text.
Which languages are supported?
Over 100, including Spanish, French, German, Chinese, Japanese, Arabic, Hindi, and Urdu. Select the language before running OCR: using the right language model dramatically improves accuracy because character shapes and dictionaries differ. The first run for a new language downloads its trained data file, so allow a moment.
Is my image uploaded to a server?
No. The OCR engine and language data run locally in your browser via WebAssembly. Images never leave your device, which makes this safe for sensitive documents, IDs, and private screenshots. An internet connection is only needed the first time to fetch the engine files.
Why is the extracted text full of errors?
OCR accuracy depends on image quality. Try a higher-resolution source, ensure text is horizontal and sharp, and enable the contrast preprocessing option. Very small text, watermarks behind text, and unusual fonts are the usual culprits. For critical documents, always proofread the output.