Free Image to Text (OCR) Converter

Yes, every tool here is free, with no account, sign-up, or usage cap. There's no hidden premium tier — what's on the page is the whole product.

No. There's no login or email capture. Open the page and every tool works immediately.

Yes, every tool is built to work on a phone screen, from the upload area to the results.

Any modern browser — Chrome, Safari, Firefox, Edge, and their mobile versions. A few tools, like OCR, need WebAssembly support, which all of these have had for years.

No. Everything, including the OCR engine, loads in the page itself the first time you use a tool.

Scanly runs the same kind of recognition technology, but in your browser instead of a native app, so there's nothing to download or update.

It's built for real use — pulling contact details off cards at a trade show, digitizing receipts for expense reports, or grabbing text from a screenshot for a document.

No. Use any tool as many times as you like, on as many files as you like.

The Image to Text OCR tool uploads the image to the OCR service so it can extract text automatically. Other browser-only tools still process files locally where noted.

Image to Text sends the uploaded image to the OCR service and receives extracted text back. There is no language model download or manual language setup.

Only upload files you're comfortable sending to the OCR service. Scanly does not add an account or saved history around the result.

No. Nothing persists between visits. Refresh the page and everything — the image, the text, the parsed fields — is gone.

The tools themselves don't log or transmit anything about your files. The page doesn't need cookies to function since there's no account or saved history.

Check your organization's policy before using OCR with confidential files, because Image to Text sends the image to the OCR service for extraction.

The Image to Text tool uses the Entertainment Couch OCR backend to read uploaded images and return extracted text automatically.

Yes. Since nothing is written to a server or local storage, closing or refreshing the tab clears it all instantly.

It reads the text visible in a photo, screenshot, or scan and turns it into text you can copy, edit, or search — the same job as a scanning app, without leaving your browser.

JPG, PNG, WEBP, and most formats your browser can display natively. HEIC photos from an iPhone usually work too, since most browsers can decode them.

Yes. Copy an image — a screenshot, or a photo from another app — and paste it directly into the drop area with Ctrl+V or Cmd+V.

Usually a few seconds for a single photo, depending on image size, connection speed, and OCR service response time.

Yes — any image works, including a screenshot. If the PDF already has selectable text, though, the PDF Text Extractor tool below will be faster and more accurate.

Not reliably. OCR reads text roughly left-to-right, top-to-bottom, so multi-column layouts or tables often need manual reordering afterward.

Only inconsistently. The underlying engine is trained on printed text, so neat block handwriting sometimes works, but cursive or messy handwriting usually won't come through cleanly.

This is usually image quality: blur, glare, small text, low contrast, or a crop that includes too much background clutter.

The OCR service detects text automatically, so there is no language menu in the Image to Text tool.

No. Upload the image and click Extract text; the OCR service handles recognition automatically.

Mixed-language images may work, but accuracy depends on the OCR service and the clarity of the text in the image.

Even lighting, sharp focus, and text that fills a good portion of the frame all help. Blur, glare, extreme angles, and very small text are the most common causes of poor results.

Generally yes, up to a point — very low-resolution or heavily compressed images lose the fine detail that letter shapes depend on.

It can help. Cropping out background clutter and keeping mostly text in frame reduces the chance the engine tries to "read" irrelevant shapes.

High contrast — dark text on a light background or vice versa — works best. Text over a busy photo or a low-contrast background is harder to separate out.

Yes — the output box is fully editable, so you can correct anything the engine got wrong before copying or downloading it.

Name, job title or company, phone number, email address, and website, when they're present on the card.

It's a best-effort read based on common card layouts, not a guarantee — always glance over the fields before saving them, especially the name and company.

Yes — every field is an editable text box, so you can fix a misread character or swap which line became the name.

It saves the scanned contact as a standard .vcf file, the format most phone and email contact apps can import directly.

It can still extract the raw text, but the automatic field-splitting works best on straightforward layouts — name, title, company, and contact details as separate lines.

Yes — use the "Show raw text" toggle below the fields to see exactly what the OCR engine read before any parsing happened.

It picks up the first phone-like number it finds. If a card lists more than one, check the raw text for the rest and add them manually.

Scan one card per image for reliable field-splitting — a photo of several cards together will run through OCR fine, but the field parser will likely mix up whose details are whose.

It looks for a vendor name, a date, and a total amount, alongside the full raw text of the receipt.

It looks for lines containing words like "total," "amount due," or "balance," and reads the currency amount closest to that word, preferring the last such match on the receipt.

Check the field and correct it by hand — receipts that list several dollar amounts close together can occasionally confuse the parser.

It reads whatever currency symbol or code appears next to the amount, but doesn't convert between currencies — the number is reported exactly as printed.

Thermal-paper receipts that have faded are one of the hardest cases for OCR generally; a well-lit, high-contrast photo taken soon after printing gives the best odds.

Common formats like DD/MM/YYYY, MM-DD-YY, and written-out dates such as "March 4, 2026" are all handled.

It's a good first pass for pulling the key numbers off a paper receipt, but always double-check the vendor, date, and total against the receipt image before submitting it.

For a text-based invoice PDF, use the PDF Text Extractor instead — it's faster and more accurate than photographing a screen.

It pulls the selectable text layer directly out of a PDF, page by page, without needing to recognize any characters visually.

This tool reads text that's already embedded in the PDF as real text. Image to Text uses OCR to recognize visible characters, which is what you need for a scanned page instead.

An empty page usually means it's a scanned image with no underlying text layer — use Image to Text on a screenshot of that page instead.

No — a PDF that requires a password to open can't be read until it's unlocked in another tool first.

It can, though very long documents — hundreds of pages — take noticeably longer since every page is processed in the browser.

No — text is extracted in the reading order the PDF stores internally, which can jumble multi-column layouts.

It extracts static text content, not the values typed into fillable form fields.

Currently it processes every page in one pass; copy the section you need from the combined output afterward.

Upload a photo or screenshot that contains a QR code, and it decodes the data stored inside it.

Anything from a plain URL to a block of text, Wi-Fi credentials, or contact details — whatever the code's creator encoded.

The code needs to be reasonably in-focus, well-lit, and not too small in the frame — try recropping closer to just the code itself.

Yes, a clean screenshot generally works even better than a photo, since there's no blur or glare to deal with.

No — if the decoded content looks like a URL, an "Open link" button appears so you can choose to visit it yourself.

It reads the first QR code it locates in the image. For multiple codes, crop and upload each one separately.

QR codes have some built-in error correction, so minor damage or a small logo overlay often still decodes fine — heavier damage will fail.

It does the same decoding, just from an uploaded image instead of a live camera feed — handy when the code is only available as a photo or file.

It combines one or more images into a single downloadable PDF, one image per page.

Yes — use the up and down arrows next to each image in the list to set the page order before you generate the file.

Yes — each image in the list has its own remove button.

A4 and US Letter, in the size dropdown above the image list; each image is scaled to fit the page while keeping its original proportions.

Images are placed at their original resolution scaled to fit the page — there's no additional compression applied.

There's no hard cap, but very large batches of high-resolution images can take a little longer to generate and produce a larger file.

Yes — any image files can be combined together in one document, in whatever order you set.

No — the output is a plain PDF with just your images, nothing added.

There's no hard-enforced limit, but very large photos, well over 15MB, may process more slowly since everything runs on your device.

The first run in a given tool or language downloads the engine and language data it needs; after that, the same tool runs faster because the browser has it cached.

The page itself loads quickly, but the first use of OCR needs to fetch a language file, so that part will be slower on a weak connection.

Recognition and PDF generation use your device's processor for a few seconds at a time, similar to running any photo-editing app briefly.

Yes, each browser tab runs independently, though doing several OCR jobs at once will compete for the same processing power.

The job simply stops — nothing is saved, so you'd need to start that file over in a new tab.

The page's own code can be cached by the browser, but each tool still needs its supporting library and any new language file, so a first-time internet connection is required per tool and language.

Extremely large images, many megapixels, take proportionally longer to process; resizing or cropping to just the relevant area speeds things up.

Yes — nothing here is restricted to personal use; they're built with exactly that kind of paperwork in mind.

Scan each card individually with the Business Card Scanner, download a vCard for each, then import the .vcf files into your contacts app in one batch.

Photograph each receipt, run it through the Receipt & Invoice Scanner, and copy the vendor, date, and total straight into your expense spreadsheet or form.

Confirm the image actually loaded — you should see a thumbnail — that you picked the right recognition language, and that the text in the photo is reasonably sharp and well-lit.

Give it a moment on a large or high-resolution image; if it doesn't finish after a minute or two, try a smaller or more tightly cropped version of the same photo.

Yes — copy the output from one tool's result box and paste it into another, since none of the tools save state between each other automatically.

Yes, both platforms' browsers support the same web technologies these tools rely on, including the camera-to-file-upload flow.

There's no built-in support channel — check that your browser is up to date first, since these tools rely on current web standards.