Image to Text (OCR)
Extract text from any image using Optical Character Recognition (OCR). Upload a screenshot, photo of a document, scanned page, or any image containing text — the tool recognises and extracts the text, making it selectable, copyable, and searchable. Supports over 30 languages including English, French, Spanish, German, Arabic, Chinese, Hindi, and more. All OCR processing runs in your browser.
- Tesseract.js — Free, open-source, runs in browser. Add to your project:
npm install tesseract.js - Google Vision API — Best accuracy, requires API key
- Google Lens — Free, drag-and-drop your image at lens.google.com
- Copy-paste from phone — iOS/Android can extract text from images in Photos app
How to Use Image to Text (OCR)
- 1
Upload your image
Click the upload area or drag and drop a JPG, PNG, WebP, or TIFF image containing text. High-contrast, well-lit images with clear text produce the best accuracy.
- 2
Select language
Choose the language of the text in the image from the dropdown. Selecting the correct language significantly improves recognition accuracy, especially for languages with special characters.
- 3
Extract text
Click the Extract Text button. The OCR engine analyses the image and identifies text regions, characters, and words. Processing takes 2–10 seconds depending on image size and text density.
- 4
Review and copy
The extracted text is displayed in an editable text area. Review for accuracy and correct any errors. Click Copy to clipboard to copy all extracted text for use in any other application.
- 5
Export
Download the extracted text as a plain .txt file, or copy it directly. Use the text in documents, spreadsheets, search engines, or any text-based workflow.
When to Use This Tool
Quick Reference
About Image to Text (OCR)
The Image to Text tool uses Optical Character Recognition (OCR) to extract readable text from photos, screenshots, scanned documents, and any image that contains text. Instead of manually retyping text from a photo, paste the output directly into documents, spreadsheets, or search fields — saving significant time on data entry and digitization tasks.
OCR text extraction is needed for:
- Extracting text from a screenshot of a PDF or document you cannot copy from
- Digitizing printed forms, business cards, or receipts from phone camera photos
- Converting a scanned book page or article into editable and searchable text
- Extracting data tables from report images for use in spreadsheets
- Reading text from screenshots of software, error messages, or chat logs
The OCR engine uses Tesseract.js — a JavaScript port of Google's Tesseract OCR engine, compiled to WebAssembly. Tesseract uses a trained neural network (LSTM-based) to recognize character patterns in image regions. The process has three stages: first, the image is preprocessed (binarized and deskewed) to improve contrast between text and background. Second, the engine identifies text regions and baseline orientation. Third, the LSTM model classifies character sequences in each region and outputs text with confidence scores. The entire pipeline runs client-side in your browser.
Input formats: JPG, PNG, WebP, BMP, TIFF, GIF. Supported languages: English (primary), with optional support for Spanish, French, German, Italian, Portuguese, and others via language model download. Output: plain text with line breaks preserved, or structured text with paragraph detection. Processing time: 5–30 seconds depending on image size and complexity. Max file size: 10 MB.
OCR processing runs entirely in your browser via WebAssembly. No image data is sent to any server. This is important for sensitive documents like medical records, financial statements, or legal documents that should not leave your device. For best accuracy, use a clear, well-lit photo with high contrast between text and background. After extracting text, use your browser's copy function to paste into any application.
Pro Tips for Image to Text (OCR)
For best OCR accuracy on photos of documents, straighten and crop the image first — even a 5° tilt significantly reduces accuracy because the text baseline deviates from horizontal.
Increase contrast before OCR if the text is faint — use the Image Enhancer in Document mode to maximize text-background contrast before extracting.
For forms with fields and labels, the tool attempts to preserve the spatial layout — copy the output into a monospace font editor to maintain column alignment.
OCR works best on printed fonts at 12pt or larger at 150+ DPI — very small text (footnotes, captions) at low resolution may be unrecognized entirely.
Frequently Asked Questions
Related Image Tools
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