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Image to Text (OCR)

Image Tools

Extract text from a photo, screenshot or scan — in 19 languages, without uploading it.

Runs entirely in your browser — nothing is uploaded

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Features

  • Nineteen languages including Hindi, Bengali, Tamil, Telugu and Urdu.
  • Layout detection for columns, or single-block mode for signs and labels.
  • Confidence score, so you know whether to trust the output.
  • Copy the text or download it as a .txt file.
  • Recognition runs on your device — the image is never uploaded.

How to use the Image to Text (OCR)

  1. 1Upload a photo, screenshot or scan.
  2. 2Choose the language of the text in it.
  3. 3Run the extraction, then copy or download the result.

Frequently asked questions

Is my image uploaded for recognition?

No, and that is unusual for OCR — almost every free service sends your image to a server. Here the Tesseract engine and the language model are downloaded to your browser and the recognition runs on your own device. The network carries the model down; it never carries your image up. That matters when the thing you are reading is an ID, a payslip or a contract.

Why is the first run slow?

The engine and the trained data for your chosen language have to be downloaded — a few megabytes, cached afterwards. Switching language fetches a different model, so the run after a language change is slow again.

The text came out garbled. What went wrong?

Almost always the image rather than the settings. OCR wants sharp, straight, high-contrast text: photograph the page flat and square rather than at an angle, crop away everything that is not text, and use the largest version you have. Check the language too — the wrong model produces confident nonsense rather than an error.

When should I turn off column detection?

For a single label, sign, receipt line or cropped phrase. Treating the image as one uniform block is more accurate than searching for a page layout that is not there. Leave it on for anything that looks like a document.

Can it read handwriting?

Barely. Tesseract is trained on printed type, so neat block capitals sometimes work and ordinary cursive does not. Handwriting recognition is a different class of model and this is not one.