Screenshots, scanned pages, photos of printed documents — text trapped inside images is a surprisingly common problem. You can see the words, but you can't select them, search them, or paste them anywhere. The manual alternative is retyping, which is slow and introduces its own errors. Online OCR is the faster option, and for most everyday inputs it works well enough that the output needs little to no correction.
How the extraction works
Optical Character Recognition analyses the pixel content of an image, identifies regions that contain text, and converts those visual patterns into actual characters. The output is plain, editable text you can copy, paste, search, and modify like anything else you'd type yourself.
The practical range of inputs is broad. Printed documents and typed text are handled reliably. Screenshots of websites, apps, or presentations work well. Photos of books, notices, signs, and forms are generally fine provided the image is sharp and well-lit. Handwriting is more variable — clear printed handwriting usually comes through accurately, while casual or stylised script may need correction.
Using Portimg to extract text
Go to portimg.com/image-to-text-ocr, upload your image — JPG, PNG, or WebP — and the tool returns extracted text within seconds. You can copy it directly from the results panel or download it as a text file. No account required, and files are deleted from the server after processing.
For documents that come as PDFs rather than images, convert them to images first using Portimg's PDF to image tool, then run the pages through OCR. This is the most reliable workflow for multi-page scanned documents.
What affects accuracy
The output quality is determined almost entirely by the image you start with, not the tool. The same OCR engine produces dramatically different results depending on how clearly the source text is captured.
Sharpness is the most important factor. Blurry text — from camera shake, being out of focus, or heavy JPEG compression — is the primary cause of OCR errors. If you're photographing a document, tap to focus on the text and hold the phone steady. Resting the phone on a surface or a stack of books eliminates camera shake entirely.
Contrast matters more than resolution. Dark ink on a white background is the easiest input for any OCR model. Faded ink, coloured paper, or shadows crossing the page all reduce the contrast between text and background, which increases errors. Even lighting — diffuse natural light rather than direct flash or a single overhead bulb — keeps contrast consistent across the whole image.
Crop before uploading. If your image includes borders, margins, table surfaces, or anything other than the text you want to extract, crop it first. Everything in the frame gets processed, and irrelevant content around the edges can occasionally affect results near those edges.
Alignment helps on multi-line documents. Text that runs at a visible angle is harder to segment into lines accurately. A few degrees of skew is usually fine, but a page photographed at a sharp angle may produce lines that run together or get split incorrectly.
Where it saves the most time
The return on effort is highest when the alternative is significant manual retyping. A full page of a printed document, a photographed article, a scanned contract, a whiteboard covered in notes from a meeting — these are the cases where OCR earns its keep. For a two-word label or a short caption, typing is faster.
Content creators use it to pull text from screenshots when there's no source file. Researchers use it to digitise physical records and make them searchable. Students extract notes from photographed textbook pages or slides. Business users digitise receipts, business cards, and forms without manual data entry.
If you have an image with text on it right now, running it through the tool takes about thirty seconds to test. The result will tell you more about what to expect from your specific inputs than any general description can.