Translate Text in Image

Two stages, two different privacy positions

Being precise about this, because "runs in your browser" would be only half true and half-true privacy claims are worse than none.

Stage one, on your device: the image is scanned and the text read out of it, using an optical character recognition engine that loads into your browser. Your photograph is never uploaded — it is processed inside the tab and nothing about the picture is transmitted.

Stage two, not on your device: the text that was found is sent to a language model to be translated, because that is where the translation happens.

So the practical rule: the picture is private, the words are not. That is a genuinely useful position for most of what people photograph — a street sign, a menu, a train timetable, a product label, a form, a museum caption. The image may contain your surroundings, your handwriting or your face, and none of that goes anywhere.

But it means the same caution as any translation service applies to the content. Do not use it on a photographed contract, a medical letter, a payslip, an ID document or anything confidential — the words in those go to a model. For that, you need a translator with an agreement in place.

Every other image translator uploads the picture as well as the text, so this is the more private option of the two available. It is just not fully private, and it is better to know which half is which.

Set the source language before you scan

The setting that most affects whether this works at all, and the one people skip.

Text recognition is substantially more accurate when it knows what language it is looking at. The engine uses the language to decide between letters that look alike, to check words against a vocabulary, and to handle accents and diacritics correctly. Left to guess, it produces plausible-looking nonsense — the right shapes assembled into words that do not exist.

So pick the language of the text in the picture, not the language you want it translated into. Those are easy to confuse and getting it the wrong way round is the commonest reason the output is gibberish.

If the image has two languages in it — a bilingual sign, a form with English labels and local answers — recognition will be weaker on whichever you did not select. Photograph and process them separately where you can.

The photograph matters more than the tool

Recognition is only as good as what it is given, and a few seconds of care beats any setting.

Get it square on. Photograph the text face-on rather than at an angle. Perspective distortion is the single biggest cause of failure — a sign shot from the side has letters that are stretched and skewed, and recognition falls apart quickly.

Fill the frame. Get close enough that the text occupies most of the picture. A small area of text in a large photograph has very few pixels per letter, and there is no recovering that afterwards.

Even light, no flash. A hard shadow across half the text, or a flash reflection on glossy paper or a screen, will hide whole words. Shade the page with your body rather than using the flash.

Hold still. Motion blur is fatal — blurred letters have no defined edges to recognise. Brace against something and take three.

Prefer flat. A page pressed flat reads far better than one curling in the middle, where the text bends and the light falls off.

And for a screenshot, take a screenshot. If the text is on a screen, capture it properly rather than photographing the monitor — you get perfect, sharp, evenly-lit text and the recognition will be essentially flawless.

What comes out, and what does not

You get text. The words, in reading order, translated. You do not get the image back with the translation overlaid on it — this is not the live camera-overlay experience some phone apps offer.

Layout is lost. Columns, tables, a form's structure, the relationship between a label and its field — none of that survives, because the output is a stream of text. For a simple sign or paragraph that does not matter. For a form or a table it means you will be working out which value belongs to which label yourself.

Handwriting mostly does not work. These engines are trained on printed type. Neat block capitals sometimes come through; ordinary cursive rarely does, and joined-up handwriting in a script you cannot read is the hardest case of all.

Stylised type struggles. Decorative fonts, logos, text over a busy photograph, low-contrast type and very condensed lettering all reduce accuracy, sometimes severely.

Check the numbers. Digits are where recognition errors do the most damage, because a wrong number looks perfectly plausible. Prices, dates, times, platform numbers, dosages and addresses are worth reading against the original image — and this is the one place a mistake here could actually cost you something.

A realistic expectation

Two failure modes to recognise, since knowing them saves time.

If the extracted text is nonsense, the translation cannot rescue it. The stages are independent — the translator faithfully translates whatever it is given, including garbled words. So when the output looks strange, the problem is almost always stage one. Retake the photograph closer and straighter, or check the source language.

And a good translation of a bad reading still reads smoothly, which is the subtle risk. A model handed a slightly garbled word will produce a confident, fluent sentence around it. So for anything that matters, look at the extracted text as well as the translation — if a word looks wrong there, it is wrong.

For a menu, a sign or a notice, that is all fine and the tool is genuinely useful. For anything consequential, treat it as a rough reading rather than a document.

The recognition engine downloads into your browser the first time you use it and is cached afterwards, so the first run is slower than the rest.

Related tools

Image to Text (OCR) reads text out of an image without translating it, which is what you want if the text is already in a language you have. AI Text Translator translates text you paste in, with a formality control. Translate PDF does the same job for a PDF with real text in it.