Background Remover
Cut the subject out of a photo and save it as a transparent PNG, processed entirely on your own device.
Turn screenshots, scanned pages and photos of printed text into editable text without sending the image anywhere.
Optical character recognition (OCR) reads the letters in a picture and returns them as plain text you can copy, search and edit. It is useful when you have a screenshot, a receipt, a scanned letter or a slide and would rather not retype it.
Everything happens on your own computer. The image is read by code running in this browser tab and is never uploaded, so it suits documents you would not hand to an online converter.
The tool uses Tesseract.js, a WebAssembly port of the open-source Tesseract OCR engine. It first cleans up the image and finds the lines and words on the page, then a trained recognition model compares the shapes of the characters against its English language data and picks the most likely letters. Because the engine is compiled to WebAssembly, it runs at close to native speed inside the browser without any plug-in.
No. The image is processed locally by Tesseract.js in this tab. Only the engine and the English language data are downloaded, and they contain no information about you.
On the first visit the browser fetches roughly 10 MB of engine and language files. They are stored in the browser cache, so later runs start much faster unless you clear your cache.
Use a sharp, well-lit image with straight text filling most of the frame. Cropping unrelated areas and using a real screenshot both help.
Cut the subject out of a photo and save it as a transparent PNG, processed entirely on your own device.
Find common objects in a photo and draw labeled boxes around them, using a detection model that runs locally.
Get a short, plain-English sentence describing what appears in a photo, generated entirely on your own device.
Transcribe English speech from an audio file or your microphone using a Whisper model that runs on your own device.
Condense a long English article or report into a few sentences, generated by a model that runs on your device.
Paste English text and see whether each line reads as positive or negative, with a confidence score for each.