Free Image to Text Converter — Best OCR Tool Online

OCR (Optical Character Recognition) is technology that analyses the patterns of pixels in an image to identify and extract text characters. Our tool uses Tesseract.js — an open-source OCR engine originally developed by HP Labs and maintained by Google — compiled to WebAssembly so it runs entirely inside your browser. The engine analyses your image locally on your device, classifies each character region, and outputs the recognised text with confidence scores for each word. Your image is never sent to any server.

The tool accepts JPEG, PNG, WebP, GIF and BMP image files. It works on scanned documents, screenshots, photos of printed text, product labels, street signs, whiteboards and books. Handwritten text is supported but accuracy is lower than printed text. For best results, images should have a minimum resolution of 300 DPI or approximately 1,000 pixels in the shorter dimension. Screenshots and screen captures typically produce excellent results.

Use our built-in image preprocessing tools before running OCR. Apply Grayscale to remove color noise. Increase Contrast to make text stand out from the background. Apply Sharpen to make blurry characters clearer. Use Invert if your image has white text on a dark background. For images with slight rotation, manually rotate before uploading. Selecting the correct language is critical — wrong language selection significantly reduces accuracy. High-resolution source images always produce better results.

Yes — completely. All OCR processing happens locally in your browser using WebAssembly. Your images are never transmitted to any server, never stored anywhere, and never sent over the internet. This makes it safe to use for confidential documents, ID images, private contracts and sensitive business materials. The Tesseract language model files (~3–5MB each) are downloaded from a CDN on first use and cached locally in your browser.

After OCR completes, our tool shows each recognised word colour-coded by confidence level. Green words (above 90%) were recognised with high confidence. Yellow words (70–90%) are likely correct but worth checking. Red words (below 70%) should be reviewed carefully as they may contain errors. The overall confidence percentage shown is the average across all recognised words. Low overall confidence usually indicates poor image quality, an unsupported font, or the wrong language setting.