Image to Text Converter (OCR)

In stages. The engine first separates text from background, then finds the layout: columns, lines, and the baseline each line sits on. Only then does recognition run, and in modern Tesseract that step is a neural network of the LSTM family, which reads a whole line as a sequence rather than guessing letters one at a time. That design is why a straight, high-contrast line of print is read almost perfectly while a photo shot at an angle fails: the tilt breaks the baseline the model expects.

On clean screenshots and flatbed scans it is very high, often near perfect on ordinary body text. Accuracy drops with low resolution, uneven lighting, JPEG artifacts around small glyphs, decorative or condensed fonts, and any rotation. Two things help more than anything else: crop to the text you actually need, and use the largest version of the image you have rather than a downscaled copy.

The engine is loaded with the English training data, which also covers digits and common punctuation. Other languages written in the Latin alphabet come out readable but lose accented characters and some accuracy, since the model was not trained on them. Alphabets outside Latin, such as Cyrillic, Greek, Arabic or CJK, are not supported, and handwriting is not recognised.

Yes, with no sign-up, no page cap and no premium tier, which is unusual for OCR. Recognition runs as WebAssembly inside your browser, so documents and screenshots are never uploaded. One honest detail: the engine and the English model are downloaded the first time you use the tool, so unlike most tools here this one needs a connection on first run.