OCR Image to Text
Extract text from any image, right in your browser. Nothing is uploaded - the OCR runs entirely on your device.
Click or drag an image here
PNG, JPG, WEBP - text-heavy images work best
OCR (Optical Character Recognition) reads the text visible in an image or scanned document and converts it into actual selectable, editable text - useful for photographed documents, scanned pages, or screenshots where you need to copy the text but can't select it directly. The recognition runs entirely inside your browser, so your images are never uploaded to a server.
Frequently asked questions
All modern browsers - Chrome, Edge, Firefox, Safari, and their mobile versions. The OCR engine is WebAssembly-based rather than relying on a browser-specific text detection feature, so there is no longer any browser requirement beyond being reasonably up to date.
The OCR engine and its English language data (roughly 7 MB combined) are downloaded the first time you click Extract Text. Your browser caches them afterwards, so later scans start much faster. A progress bar shows what is happening during the download and the recognition itself.
No. The entire process runs on your own device - the image is never sent to this site or to any third-party service. That also means the tool keeps working on a slow connection once the engine has been cached.
Clear, well-lit photos or scans with straight, in-focus text work best. Blurry photos, extreme angles, low resolution, or unusual/stylized fonts reduce accuracy. Cropping tightly to just the text you need usually improves the result.
OCR is designed for printed/typed text. Handwriting recognition is a much harder problem and generally isn't reliable with standard OCR - results on handwritten notes will likely have significant errors.
The tool currently recognises English. It handles the standard Latin alphabet well, but other scripts such as Arabic, Urdu, Chinese or Cyrillic need their own language data and are not supported yet.
OCR is very good but not perfect - always proofread the extracted text against the original image, especially for numbers, unusual words, or lower-quality source images.