Image to Text – Extract Text From Images With OCR
About the Image to Text Converter
The Image to Text converter reads printed and typed characters out of an image and gives you back editable text. It runs Tesseract, the open-source OCR engine originally developed at HP and now maintained by Google, compiled to WebAssembly so the whole recognition process happens inside your browser tab. That matters for anything sensitive: scanned invoices, ID documents, medical paperwork, and internal screenshots are all processed on your own machine rather than sent to an OCR service.
Seventeen languages are available, including English, Spanish, French, German, Russian, Arabic, Hindi, Japanese, Korean, and both Simplified and Traditional Chinese. The engine downloads a trained data file the first time you use a given language, which takes a few seconds, then caches it for the rest of your session. Switching languages triggers a fresh download for that language on the next extraction.
Each result comes with a confidence score, which is the engine’s own estimate of how certain it is about the characters it produced. Scores above 85% usually mean clean, near-perfect output; anything below 60% is worth reading carefully, since OCR errors tend to be plausible-looking substitutions rather than obvious garbage. The extracted text lands in an editable box so you can fix mistakes before copying or downloading.
Results depend heavily on the source image. High-resolution scans of printed documents work extremely well; photos of curved book pages, low-light receipts, and handwriting work poorly. Tesseract is trained on printed type, so cursive and casual handwriting are largely outside what it can do. If your image has a lot of irrelevant background around the text, the Image Cropper lets you isolate just the text block first, which noticeably improves accuracy.
Members of the BigToolSite team benchmarked this against flatbed scans, phone photos of receipts, code screenshots, PDF page exports, and non-Latin scripts, tracking how confidence scores moved with and without the enhancement option and confirming that the extraction step never sends image data off the device.
Extracted text usually needs cleanup, and the Grammar Checker is a practical next step for catching the substitution errors OCR tends to introduce in longer passages.
If you are digitizing documents to hit a length target or bill by volume, the Word Counter gives you a detailed breakdown beyond the running totals shown here.
How to Use the Image to Text Converter
- Drag up to 10 images onto the upload area or click to browse. Each file can be up to 10MB, in any format your browser reads, including HEIC from iPhones.
- Select the Language that matches the text in your image. Picking the wrong language significantly degrades accuracy, since each language uses its own character model.
- Leave Enhance image for better OCR checked for most sources. It converts the image to grayscale and boosts contrast by 1.5×, which helps separate faint or low-contrast text from the background. Uncheck it if your source is already a clean black-on-white scan.
- Click Extract Text on any image. The first extraction initializes the OCR engine and downloads the language data, shown as a progress percentage in the status banner.
- Watch the per-image progress bar. When it finishes, the status line shows the confidence score, for example “Done! Confidence: 92%”.
- Read and edit the extracted text directly in the box. It becomes editable once extraction completes.
- Use Copy to put the text on your clipboard or Download TXT to save it as a file named after the source image.
- For a batch, click Extract Text from All to process every pending image in sequence, then Download All to get one ZIP containing a separate .txt file per image.
- The summary strip tracks images loaded, images processed, and total characters and words across everything extracted so far.
Example Usage
Input: receipt.jpg, a phone photo of a printed store receipt
Settings: Language English, enhancement enabled
Output: the line items, prices, and totals as plain text, with a confidence score around 80% to 90% for a well-lit, flat receipt
For a screenshot of code or an error message you cannot select and copy, the same process typically returns confidence above 95%, since screen-rendered text is perfectly sharp and evenly lit. This is the single most reliable use case for browser OCR.
A multi-page scan works well as a batch. Load all 10 pages, click Extract Text from All, and download the ZIP. Each page arrives as page-01-text.txt, page-02-text.txt, and so on, matching the source filenames so the order stays intact.
For a Japanese menu or a Russian sign, change the Language dropdown before extracting. The engine downloads that language’s trained data once, then reuses it for every subsequent image until you switch again.
Frequently Asked Questions
Optical Character Recognition analyzes the shapes of light and dark regions in an image, matches them against trained character models, and outputs the letters and numbers it identifies. It converts a picture of text into text a computer can search, copy, and edit.
No. The Tesseract engine runs as WebAssembly inside your browser, so images are processed on your own device. Only the language data file is downloaded, and that comes from a public CDN.
The OCR engine and the trained data for your chosen language have to download and initialize on first use. Subsequent extractions in the same language are much quicker because everything is already loaded.
On clean, high-resolution scans of printed text, accuracy commonly exceeds 95%. Phone photos, low-contrast receipts, unusual fonts, and skewed angles reduce it considerably. The confidence score shown after each extraction tells you how certain the engine was.
Not reliably. Tesseract is trained on printed and typed characters, so neat block printing sometimes works while cursive and casual handwriting generally do not.
Use the highest resolution source you have, make sure the text is level rather than tilted, crop away background clutter, and pick the correct language. The enhancement option helps with faint or low-contrast text.
It is the engine’s own estimate of how sure it is about the recognized characters, expressed as a percentage. Treat anything under 60% as needing a careful proofread, since OCR mistakes often look like real words.
Seventeen: English, Spanish, French, German, Italian, Portuguese, Russian, Japanese, Simplified and Traditional Chinese, Arabic, Hindi, Korean, Dutch, Polish, Turkish, and Vietnamese.
Yes. The result box becomes editable once extraction finishes, so you can correct errors before copying or downloading. Your edits are included in the downloaded file.
Only loosely. Line breaks are generally retained, but multi-column layouts and table structures are flattened into sequential lines, so complex documents need manual restructuring afterward.
