Extract text from screenshots, scanned notes, photos and document images. Review the result before using it—OCR is clever, not psychic.
Choose a clear image to begin.
Extract text from an image online
This free image-to-text tool uses OCR (optical character recognition) to turn visible words into editable text. It can help with screenshots, printed notes, receipts and document images. Clear, high-contrast images work best.
It may recognise neat handwriting, but printed text and clear screenshots are much more reliable.
Why are some words wrong?
Blur, glare, unusual fonts and tiny text can lower OCR accuracy. Always review important details.
Is this tool free?
Yes. It is free to use and does not require an account.
Get cleaner OCR results from clearer source images
Image-to-text extraction works best when characters are sharp, upright and easy to distinguish from the background. Screenshots and scanned documents with strong contrast usually perform better than blurry camera photos, curved pages or text covered by shadows.
Prepare difficult images first
Crop away unrelated areas, rotate the image so lines are horizontal and use the highest-resolution source available. If the text is tiny, avoid repeatedly compressing the image before extraction because compression artifacts can distort characters.
Always proofread extracted text
OCR can confuse similar-looking characters such as O and 0, l and 1, or punctuation marks. Numbers, names, addresses, code and financial values deserve extra checking because a single incorrect character can change the meaning.
Respect sensitive documents
Identity cards, medical records, invoices and private correspondence can contain sensitive information. Use a trusted device, avoid unnecessary sharing, and delete temporary copies when you no longer need them.
How browser OCR turns pixels into characters
This page uses Tesseract.js to run optical character recognition from the browser. After you select a file, the image is passed to the OCR engine as image data rather than being submitted through an ITFixLab upload form. The engine analyses the page as pixels, looks for text-shaped regions, and returns recognised characters with spacing and line breaks. OCR is probabilistic, so the returned text is an interpretation of the image—not a guaranteed transcription.
Why OCR accuracy changes from one image to another
Recognition depends heavily on the signal available in the source. Small text gives the engine fewer pixels per character, motion blur smears letter edges, and shadows or textured backgrounds reduce contrast. Similar glyphs are a common failure point: O and 0, I, l and 1, or commas and periods can be difficult to separate. Rotating a page upright, cropping irrelevant areas and using an uncompressed source can improve the character shapes the engine receives.
Language model and layout limitations
The current implementation recognises English with the eng language model. It is best suited to normal printed text and straightforward document layouts. Decorative fonts, dense tables, multi-column pages, handwriting and mixed-language documents can produce reordered or incorrect output. The tool returns plain text, so it does not preserve the original page design, font styling or exact table structure.
CPU, memory and privacy boundaries
OCR is heavier than a simple image conversion because the browser must decode the image and run recognition work on your device. Large camera photos can therefore use noticeable CPU and memory and may take longer on phones. Tesseract.js and its OCR resources are loaded as browser dependencies, but this page does not send the selected source image to an ITFixLab server for recognition. For sensitive material, still review your browser, device and network environment before processing it.
