Image to Text Converter (OCR)

Common Use Cases for Image to Text OCR

Optical character recognition turns pixels into editable, searchable text. Here are the scenarios where it saves the most time.

Digitizing paper documents. Scan contracts, receipts, invoices, or handwritten notes with your phone camera, then upload the image here to pull out every line of text. The result is a clean plain-text file you can paste into a spreadsheet, email, or document management system without retyping a single word.

Extracting text from screenshots. Product descriptions, error messages, social media posts, chat logs, anything you can screenshot, you can convert. Paste directly from your clipboard with Ctrl+V or Cmd+V and the OCR engine processes it immediately.

Converting scanned PDFs to searchable text. Many older PDFs are just images wrapped in a PDF container. Upload them here and the tool detects that the PDF is image-based, renders each page, and runs OCR on every one. The output is a single text file covering all pages.

Translating foreign-language images. Select from over 20 OCR languages, including Chinese, Japanese, Korean, Arabic, and Hindi, extract the text, then paste it into any translation service. This two-step workflow is far faster than manually transcribing characters you may not be able to type.

Accessibility and assistive technology. Extract text from infographics, slide decks, or photographs of signs so screen readers can process the content. OCR bridges the gap between visual media and text-based assistive tools.

Getting a clean read

This tool uses Tesseract.js, a JavaScript port of the Tesseract OCR engine originally developed by HP and now maintained by Google. When you upload an image, the engine loads the trained language model for the language you selected, segments the image into lines and words, and matches each character against its recognition database. The entire process runs inside your browser tab using WebAssembly, which means your files are never uploaded to a remote server for image-based OCR.

Accuracy depends on the quality of the input. Clean, high-contrast images of printed text typically achieve 95-99% accuracy. Photographs taken at an angle, images with heavy compression artifacts, or low-resolution scans will produce lower accuracy. Handwritten text is recognized but results vary significantly depending on legibility, neat block letters work best, while cursive scripts remain challenging for any OCR engine.

For best results, crop the image to include only the text area, ensure even lighting with no shadows across the page, and use the highest resolution available. If you are working with a multi-page scanned PDF, the tool automatically renders each page as an image and runs OCR sequentially, combining all extracted text into a single output.

The first time you select a new language, the corresponding trained-data file is downloaded and cached by your browser. Subsequent uses of the same language start almost instantly. Language models range from about 1 MB to 15 MB depending on the script complexity.