Afbeelding naar Tekst (OCR) — Gratis online tool

Free Online OCR — Extract Text from Images

Toololis Image to Text uses Tesseract.js, the most widely used open-source OCR (Optical Character Recognition) engine, to extract text from images directly in your browser. Nee server uploads, no account required, and no limits on usage. Your images stay completely private because all processing runs locally on your device.

Whether you need to digitize a printed document, extract text from a screenshot, copy a quote from a photo, or convert a scanned receipt into editable text, this tool handles it in seconds. Simply upload your image, select the text language, and let the OCR engine do the work.

Key Functies

• Client-side OCR — all text recognition happens in your browser using Tesseract.js v5. Your images are never sent to any server.
• 15 languages supported — English, German, French, Spanish, Italian, Portuguese, Dutch, Polish, Russian, Chinese, Japanese, Korean, Arabic, Hindi, and Turkish.
• Multiple image formats — upload JPG, PNG, WebP, or BMP files. Drag and drop or use the file picker.
• Progress tracking — watch a real-time progress bar as the OCR engine loads language data and processes your image.
• Copy and download — one-click copy to clipboard or download the extracted text as a .txt file.
• Drag and drop — drop images directly onto the upload area for a fast workflow.

How OCR Technology Works

Optical Character Recognition (OCR) is the technology that converts images of text into machine-readable text. The process involves several steps: first, the image is preprocessed to improve contrast and remove noise. Then the engine segments the image into lines, words, and individual characters. Each character is compared against trained patterns for the selected language, and the most likely match is chosen. Modern OCR engines like Tesseract use neural networks (LSTM) to achieve high accuracy even with complex fonts and layouts.

Tesseract.js brings Google's Tesseract OCR engine to the browser via WebAssembly. It downloads trained language data files from a CDN on first use (typically 1-15 MB depending on the language), then processes images entirely on your device. This means your data remains private, and the tool works offline once the language data is cached.

Tips for Best OCR Results

• Use high resolution — images with at least 300 DPI produce the best results. Avoid blurry or low-resolution photos.
• Good contrast — dark text on a light background works best. Avoid images with colored backgrounds behind text.
• Straight alignment — text that is rotated or skewed reduces accuracy. Straighten images before uploading if possible.
• Select the right language — choosing the correct language dramatically improves recognition accuracy for non-English text.
• Simple layouts — single-column text works best. Complex multi-column layouts or text overlaid on images may have lower accuracy.
• Clean images — remove watermarks, stamps, or other visual noise that overlaps with the text you want to extract.

Common Use Cases

OCR technology is useful in many everyday scenarios. Students and researchers use it to extract quotes from textbook photos and scanned papers. Business professionals digitize printed invoices, receipts, and contracts. Developers extract text from screenshots for debugging or documentation. Travelers photograph signs and menus in foreign languages for translation. Content creators convert printed materials into digital text for blog posts and social media.

This tool is especially useful when you receive a document as a photo or screenshot and need the text in a copyable format. Instead of manually retyping everything, upload the image and get the text in seconds. The downloaded .txt file can then be opened in any text editor, word processor, or pasted into emails and documents.

Browser Compatibility

Tesseract.js runs in all modern browsers including Chrome, Firefox, Edge, and Safari. It uses WebAssembly for performance-critical operations. The first time you use a specific language, the tool downloads the trained data file (1-15 MB), which is cached by your browser for subsequent uses. Processing speed depends on your device's CPU — modern laptops process a typical document image in 5-15 seconds.