How to Extract Text from an Image Online

OCR, short for optical character recognition, turns visible text in an image into text you can copy, edit and search. It is useful for screenshots, receipts, notes, scans and photographs of documents. A browser-based OCR tool is a quick option when you only need the text and do not want to install an application.

Extract text from an image

1. Open the Image to Text OCR tool.
2. Choose a JPG, PNG or WebP image, then select the language that matches the text.
3. Select Extract Text, review the result and use Copy Text when it is ready.

Improve OCR accuracy

OCR works best when characters are large, sharp and separated from the background. Crop out decorative areas, use a straight image, and avoid blurry photographs. A scan or screenshot normally produces better results than a low-light phone photo. Choose the correct OCR language before processing; this helps the engine distinguish similar letter shapes.

Always proofread the result

OCR is an interpretation of an image, not a perfect transcription. Check names, numbers, dates, addresses and accented characters before using the extracted text in an important document. For a long scan, compare a few lines at the beginning, middle and end.

Privacy and file limits

The current NeroTool OCR workflow runs in the browser and does not intentionally upload your image to a NeroTool server. Images up to 15 MB are accepted to keep the process responsive on ordinary devices.

Know when OCR needs proofreading

OCR can misread characters that look similar, especially in low-resolution images, unusual fonts, tables and noisy backgrounds. After extraction, compare names, numbers and other important values with the source image. For administrative or financial text, a manual verification step should be treated as part of the workflow rather than an optional extra.

OCR can recover text, but recognition is not the same as copying text

Text extraction from an image relies on recognizing characters from pixels. Clear scans with high contrast generally work better than photographs with perspective, shadows or decorative backgrounds. Tables, handwriting and unusual fonts can require additional cleanup.

Verify names, numbers and punctuation

OCR errors are often subtle: a zero can become the letter O, a one can become lowercase l, and punctuation can disappear. Compare the extracted text with the source image when accuracy matters. If the image contains a table, also check whether the reading order matches the visual layout.

Treat OCR output as a draft for important documents, not as unquestionable source text.

Know when OCR output needs manual correction

OCR is strongest on clean, high-contrast printed text and less reliable on handwriting, decorative fonts, curved surfaces and low-resolution photos. For invoices, IDs, addresses or other data where one character matters, compare the extracted result directly with the image. If the text will be imported into another system, also check line breaks and column order before treating it as structured data.

Separate transcription from interpretation

OCR can tell you which characters appear to be present, but it does not know whether an extracted number is a correct invoice amount or whether a word belongs to the right column. Preserve the original image and verify the semantic meaning of important fields after extraction. This is where a short manual review adds significant value.