OCR — Optical Character Recognition — is the technology that converts text inside an image into actual, selectable, editable characters. It's been around for decades, but the accuracy and accessibility of browser-based tools have improved to the point where it's genuinely useful for everyday tasks rather than just enterprise document processing.
This piece covers how Portimg's image-to-text tool works, what kinds of inputs it handles well, and how to get reliable output without much trial and error.
The problem it solves
Text inside an image is invisible to a computer in the way that matters for editing and searching. A JPEG of a printed contract looks like text to you, but to any software it's just a grid of coloured pixels — no different from a photograph of a landscape. You can't select a word, copy a sentence, or search for a term. OCR bridges that gap by analysing the visual content and reconstructing the text as actual characters.
The output is plain text you can paste anywhere: a document, an email, a spreadsheet, a translation tool. Once it's text, it behaves like text — editable, searchable, reformattable.
What Portimg's OCR handles
The tool accepts JPG, PNG, and WebP files. It works across a range of common inputs:
Printed documents and typed text are the strongest case — clean scans of typed pages convert with high accuracy, typically requiring minimal correction. Screenshots of websites, applications, and presentations work well for the same reason: high-contrast, consistently-rendered text on a plain background. Photos of physical documents — printed reports, forms, letters, business cards — are reliable when the image is sharp and evenly lit. Handwriting is supported but variable; clear, consistently-formed handwriting often comes through well, while informal or stylised script will need more review before use.
Multilingual text is handled across a range of languages. If you're extracting from a document in a non-Latin script, it's worth testing a sample page first to calibrate expectations for your specific language and font combination.
Using the tool
Visit portimg.com/image-to-text-ocr, upload your image, and the extracted text appears in a results panel within seconds. Copy it directly or download it as a text file. No account is required, and images are deleted from the server immediately after processing — relevant when the document contains personal or confidential information.
Image preparation
The single most reliable way to improve OCR output is to improve the image before uploading rather than correcting the text after. Four things consistently matter:
Focus and sharpness. Blurry letterforms are the most common source of substitution errors — 'l' read as '1', 'O' as '0', 'B' as '8'. If you're photographing a document, tap to focus on the text before capturing and hold the camera steady. A tripod or a stack of books eliminates camera shake entirely.
Contrast. Dark ink on white paper is the ideal input. Anything that reduces that contrast — faded ink, coloured paper, shadows crossing the page — increases error rates in the affected areas. Diffuse natural light from the side keeps contrast even across the whole image. Flash tends to create a bright hotspot in the centre and shadow at the edges, which is the opposite of what you want.
Alignment. Text running at a visible angle is harder to segment into lines accurately. A few degrees of skew is usually tolerable, but a page photographed at a sharp angle can produce garbled output. Rotate and straighten the image before uploading if it's noticeably tilted.
Cropping. If the image includes wide margins, the surface the document is resting on, or anything other than the text itself, crop it first. Everything in the frame gets processed, and extraneous content near the edges occasionally affects results in those areas.
Fitting it into a workflow
For documents that arrive as scanned PDFs — where text looks selectable but isn't — convert the pages to images first using Portimg's PDF to image tool, then run the images through OCR. For documents that need contrast or exposure adjustment before conversion, the image editor handles that in the same browser session.
The combination covers most real-world document digitisation tasks: a scanned PDF with poor contrast, an old letter photographed on a phone, a whiteboard covered in notes, a stack of receipts. Run the preparation steps, upload, and the output is usually usable with light correction at most.
If you have a document in front of you that you'd otherwise retype, running it through takes under a minute to test.