Extract text from images using OCR with support for multiple languages.
An image can be full of words and still contain no text: the words are pixels, so you cannot select, search, copy, or edit them. This tool runs optical character recognition on the image you provide and returns the words it can read as plain, copyable text, with support for multiple languages. The thing people most often get wrong is expecting understanding: OCR reads characters, not meaning, and its accuracy depends on how clearly the text is rendered. Clean screenshots and flat scans usually come back accurate; blurry, angled, or low-resolution photos come back garbled.
A concrete input and expected output from the current implementation.
Input
An image upload: a 1000-pixel-wide screenshot of a plain white page containing the single line 'Hello World' in a clean black sans-serif font.
Expected output
Hello World
On a clean, high-resolution, correctly oriented image of simple printed text, character recognition is effectively deterministic, so the returned text is exactly the line the image shows. That certainty is what image quality buys you: losing any of those conditions is when errors appear.
The image is read as a grid of pixels and broken into regions that look like lines of text. Characters are recognized individually, then word-level context corrects ambiguous ones, so a smudged 'e' still reads as 'e' when the letters around it form a real word. Results are emitted in visual order, line by line from top to bottom.
Recognition models are language-specific, so the languages of the document must be active before recognition runs. With the right model loaded, Latin, Cyrillic, and other scripts are read correctly; without it, text comes back as nonsense or as the nearest-looking Latin characters. Enabling several languages at once handles mixed documents but can slow the run and add errors.
Not reliably. OCR is built for printed text; casual handwriting, cursive, and hurried notes are usually misread or returned as fragments. Carefully printed block letters on a clean, high-resolution page can come through, but treat any handwritten result as unverified until you have checked it against the original.
The image, almost always. Too few pixels per character, an angled shot, shadows or glare, and small or low-contrast text all break recognition. Reshoot or rescan at higher resolution, straighten and crop the page, and retry. If the document mixes languages, make sure every language it contains is selected.
The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.
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