Image to Text
Image & Video Tools
Extract text from any image in seconds with fast, accurate OCR. Free in your browser, no signup, and nothing you upload is stored.
Image to Text
Powered by AI vision. Free. No signup.
How image-to-text actually works
Optical character recognition reads the shapes in a photo or scan and converts them into editable, selectable characters you can copy and search. It only works on text that is actually written in the image; it cannot guess what a blurry or handwritten word should be, so it transcribes what it sees. The thing most people miss: accuracy depends almost entirely on input quality. A flat, high-contrast, straight-on shot of printed type converts cleanly, while a tilted phone photo of a glossy receipt under bad lighting produces garbled lines, dropped characters and merged words every time.
Image-to-text tips
- Crop tight around the text and shoot straight-on; skew, glare and shadows are what break OCR accuracy most often.
- Printed, high-contrast fonts convert best, but cursive handwriting and decorative display fonts stay unreliable no matter the resolution.
- Higher resolution helps up to a point; a sharp, well-lit smaller image beats a huge blurry one every time.
- Always proofread numbers, punctuation and similar shapes like O versus 0 or l versus 1 before trusting the output.
Related tools
Image to Text — common questions
Latest questions readers ask us about this topic.
Can image-to-text read handwriting?
It depends heavily on the writing. Neat, separated print can convert reasonably well, but cursive, overlapping or stylised handwriting usually produces errors. OCR is built for typed text, so handwritten notes always need careful proofreading afterward.
What image formats work best for OCR?
Common formats like JPG, PNG and screenshots all work. What matters more than the format is sharpness, contrast and a straight, well-lit shot. PNG screenshots of on-screen text typically convert most cleanly because they avoid camera blur entirely.
Why is the converted text full of errors?
Usually the source image is the problem: low resolution, glare, a tilted angle, low contrast or an unusual font. OCR transcribes exactly what it can resolve, so a clearer, flatter, better-lit image will dramatically reduce mistakes.
Related questions
The sub-questions readers ask next — answered, with where to go.
Specificity and tension. A scroll-stopping opener promises a concrete payoff ('the 3-word edit that doubled my reply rate') or opens a loop the reader needs closed — not a vague 'let's talk about engagement'. Front-load it: on most feeds only the first line shows before a cut-off, so the hook has to do its work there. Test several angles for the same post; the winner is rarely the one you'd have guessed.
Style your opening lineMatch the length to the job, then check it against the limit. Instagram captions can run long for storytelling but the hook must land in the first ~125 characters before 'more'; X/Twitter rewards tight, standalone lines; LinkedIn truncates around two lines. TikTok and Reels captions are short by nature. The reliable move is to draft freely, then trim against a live counter so nothing important gets cut.
Check the limit liveFewer, and more relevant, than the old advice. The era of 30 generic tags is over — most platforms now reward a small set (roughly 3–8) that genuinely describe the post, mixing one or two broad tags with several specific, lower-competition ones. Stuffing tags reads as spammy and can suppress reach. Put them where they don't interrupt the read: end of the caption or first comment.
Read the content hubTreat the bio as a one-line pitch, not a résumé. Open with who you help and the outcome they get, add a single proof point, and close with a reason to follow or a clear next step. Keep it skimmable, lead with the words people would search, and reserve any styled text for one emphasised phrase. Links and @mentions stay plain so they stay clickable.
Generate a bioExplore the topic cluster
A wider set of tools and guides on this topic.