How to convert an image to text
- Drop up to 10 images into the box above or press Browse files.
- Pick the language of the text. This choice changes the result.
- Leave Prepare the image on unless an image reads better without it.
- Press the button and wait. The first run downloads the engine once.
- Check the confidence score and proofread the text before you use it.
What this OCR tool gives you
- Printed text in 16 languages including Arabic, Bengali and Japanese.
- A confidence score so you know when to read more carefully.
- Up to 10 images per run with results labeled and in order.
- Rejoined words that a line end split with a hyphen.
- An editable result box because no OCR engine is perfect.
- Recognition on your own device so private papers never leave it.
What does an image to text converter do?
An image of a page is just a grid of colored dots to a computer. It holds no letters and no words. You cannot search it.
OCR stands for optical character recognition. It looks at that grid and finds the shapes that are probably letters. Then it decides which letter each shape most likely is. The result is real text. You can edit and search it. You can also translate it and count it.
A language model does the deciding. Many letters look the same alone. A capital I, a small l and the number 1 can be the same straight line. Context tells them apart. That is why the language menu changes the result and not just the labels.
What image to text OCR reads well
| Result | Image type |
|---|---|
| Excellent | Screenshots, PDF pages saved as images and flatbed scans |
| Good | A steady phone photo of a page taken straight on in good light |
| Mixed | Photos at an angle, faded receipts and text on busy backgrounds |
| Poor | Handwriting, fancy display fonts and blurry images |
Resolution matters more than most people expect. Letters need to be about 20 pixels tall before OCR gets reliable. A normal screenshot often beats a photo taken from across a desk. Got a poor result? Take the picture again closer and straighter. That helps far more than any setting.
How this tool prepares your image
Dark backgrounds
OCR engines learn on dark text printed on light paper. Give one yellow letters on black or a dark mode screenshot and accuracy crashes. This page measures the image first. It inverts a mostly dark image. So light text on black becomes dark text on white. It also enlarges small images because tiny letters give the engine too little to read.
Colored text
A normal gray conversion copies how the eye sees brightness. It weighs green heavily and red very little. So red letters on black turn almost as dark as the background. The engine then skips them. The obvious fix breaks a different case. Black text on a bright green highlight turns muddy.
One rule cannot serve both. So this page builds three gray versions and scores each one. It picks the version that splits ink from paper most cleanly. In our tests on a green highlighted image, that change took the result from one word to more than two hundred.
Text over a photo
A bright sky at the top and a dark ground at the bottom need different cutoffs. So the tool judges each pixel against its own surroundings when the background varies. In our test of white and red text over a railway photo, the word count rose from 69 to 125. A missing title came back too.
Large headings
Many OCR setups treat a page as one even block of text. That drops big headings on posters with no warning. This page treats the image as a single column with mixed text sizes. Across eight test posters the default setting found one heading. Our setting found all eight.
How to read the confidence score
- Above 85 percent: the text is usually clean after a quick skim.
- 70 to 85 percent: expect scattered errors in numbers and punctuation.
- Below 70 percent: improve the image rather than fix the text.
Proofread anything important at any score. A wrong digit in a phone number or an invoice total can cause real trouble. That is exactly the kind of error OCR makes. So the output box stays editable.
Why this image to text tool downloads a file
Every other tool on this site works with the code already in the page. OCR needs a trained model. Each model is several megabytes. So the engine and the one language you pick download when you press the button. Your browser caches them for next time.
The key point is what never goes out. Your images stay in this tab. A public model comes down. None of your data goes up.
What to do with the text afterward
OCR text often needs a tidy. The text cleaner removes odd characters. The line break remover joins lines into paragraphs. The word counter tells you what you got. Is your source a PDF? Try PDF to text first. A PDF with a real text layer gives a perfect result with no OCR at all.