Image to Text (OCR)
Extract text from images and scanned documents with AI-powered OCR — runs entirely in your browser.
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PNG, JPG, WebP, PDF screenshots
Extracted Text
Image to Text (OCR) — Extract Text from Any Image
This tool uses Tesseract.js, a pure-JavaScript port of the industry-standard Tesseract OCR engine, to recognise text in images entirely within your browser. It supports printed text in English across JPG, PNG, WebP, and screenshot formats. The OCR data file downloads once (~10 MB) and is cached locally. Accuracy is best on high-contrast, clearly typeset text at 72 dpi or higher — handwritten text is not currently supported. Confidence score indicates how certain the engine is about the overall recognition result. Nothing is uploaded to a server.
Built and maintained by Meet Shah · Last updated
What this tool is used for
- Pulling text out of a screenshot so you can search or quote it.
- Extracting a code or a reference number from a photographed document.
- Getting the text from a scanned page that has no text layer.
- Copying an error message from a screenshot someone sent you.
- Extracting a table's contents from an image for further processing.
Frequently Asked Questions
- How does the text extraction work?
- Optical character recognition running in your browser through WebAssembly — the image is segmented into lines and characters, each is matched against trained models, and a language model resolves ambiguities. Nothing is uploaded, which is the practical reason to do it locally.
- Why is the first run slow?
- Because the OCR engine and its language data have to download and initialise — several megabytes, once, then cached. That one-off cost is what buys the ability to run offline and on documents you would not send to a remote API.
- What affects accuracy most?
- Resolution and contrast, far more than the engine. 300 DPI of clean black text on white is near-perfect; a phone photo of a screen at an angle is not. Straightening, cropping to the text and raising contrast before running OCR usually helps more than any setting.
- Which characters are most often misread?
- The homoglyph families: 0 and O, 1 and l and I, 5 and S, 8 and B, rn read as m. That is why OCR output of a serial number or a code needs checking in a way that OCR of a paragraph does not — prose has context to correct against and an identifier has none.
- Does it preserve layout?
- Reading order, mostly; layout, no. Columns, tables and captions are flattened into a stream, so a multi-column scan often interleaves lines from both columns. Cropping each region separately is the reliable workaround for anything with a real layout.
Common errors and gotchas
- Expecting accuracy from a low-resolution or skewed source, where OCR degrades sharply.
- Assuming layout is preserved. Columns and tables usually come back as running text.
- Trusting digits and punctuation, which are where OCR errors concentrate and matter most.
- Overlooking that the first run downloads a model, so it needs one online use before working offline.
- Using it on handwriting, which needs a different class of model entirely.