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Image to Text (OCR)
Extract text from receipts, screenshots and document images with multi-language OCR and preprocessing, drag and drop to start, copy or download results.
Image to Text (OCR)
Drop image here or click to upload
Recognition Result
Docs
Image to Text (OCR) is a web-based optical character recognition tool built on Tesseract.js, capable of extracting text from images in over 100 languages.
Main Features
Multi-language Recognition
Supports recognizing text from images in:
- Chinese (Simplified, Traditional)
- English, Japanese, Korean
- French, German, Spanish, Portuguese
- Italian, Russian, Arabic and 100+ other languages
Image Upload
- Drag and drop image upload
- Click to select files
- Supports major image formats (JPG, PNG, GIF, BMP, WebP, etc.)
Recognition Settings
- Auto language detection mode
- Manual language selection
- Real-time status display
Result Processing
- Display complete recognized text
- One-click copy to clipboard
- Download as text file
How to Use
- Open the page, drag an image to the upload area, or click “Select Image”
- Choose the language to recognize (auto detection supported)
- Click “Recognize” to start processing
- Wait for recognition to complete, view results in the right panel
- Click “Copy” to copy results, or “Download” to save as text file
Use Cases
- Document digitization: Convert paper documents, scanned files to editable text
- Image text extraction: Extract text from screenshots, photos
- Business card recognition: Quickly extract contact information
- License plate recognition: Identify vehicle plate numbers
- Translation preparation: Convert foreign document images to text for translation
Technical Description
- Recognition Engine: Tesseract.js (based on Tesseract OCR)
- Runtime: Browser local processing, no server required
- Privacy Protection: All image processing is done locally, no upload to server
- Language Models: Language packs auto-download on first use (approx. 2-10MB)
Notes
- First recognition requires downloading language pack, please maintain network connection
- Higher image quality results in better recognition accuracy
- Complex backgrounds or low-resolution images may affect recognition
- Handwritten text recognition may not be as effective as printed text
Last verified: 2026-04-06 Source: naptha/tesseract.js