Is my image uploaded to a server for OCR extraction?

No. The optical character recognition happens entirely within your web browser using a local WebAssembly port of Tesseract.js. Your images never leave your device.

What image formats are supported for text extraction?

The tool supports standard image formats including JPEG, PNG, WEBP, and BMP.

Why is the extracted text missing characters or incorrect?

OCR accuracy depends heavily on image quality. Ensure your image has high contrast, is properly aligned without skewed text, and has a sufficient resolution (ideally 300 DPI) for the best results.

Can this tool read handwritten text?

The underlying Tesseract engine is highly optimized for printed, typed fonts. While it can occasionally read very neat block handwriting, cursive or messy handwriting will result in low extraction accuracy.

Image to Text (OCR) - Free Client-Side Extractor

How user-Side Optical Character Recognition (OCR) Works

Optical Character Recognition (OCR) is the process of analyzing an image to identify alphanumeric characters and convert them into machine-readable text. Historically, robust OCR required massive server clusters to process the image arrays, forcing users to upload sensitive documents—like invoices, medical records, or legal contracts—to remote third-party servers.

The Power of WebAssembly and Tesseract.js

This tool completely eliminates privacy risks by utilizing a WebAssembly (Wasm) port of the legendary Tesseract OCR engine. Originally developed by Hewlett-Packard and currently maintained by Google, Tesseract is highly regarded for its accuracy. By running inside your browser's sandbox environment, the extraction process occurs entirely on your device's local CPU and RAM.

Optimizing Images for Best OCR Results

While the AI engine is highly resilient, you can drastically improve the extraction accuracy and speed by following a few simple preparation rules before dropping your image into the tool:

High Contrast: Black text on a stark white background yields the highest accuracy. If you are photographing a document, ensure it is well-lit and free of dark shadows.
Proper Alignment: Text that is heavily skewed, rotated, or warped (like a crumpled receipt) forces the engine to guess character boundaries. Crop and straighten your images if necessary.
Resolution: The sweet spot for OCR is generally 300 DPI (Dots Per Inch). If an image is too small or heavily pixelated, the neural network will struggle to differentiate similar characters, like '0' (zero) and 'O' (the letter), or '1', 'l', and 'I'.