How to use Image to Text (OCR)
Extract text from images, screenshots, and scanned photos using on-device OCR (Optical Character Recognition) powered by Tesseract.js. Supports English and a wide range of languages. Your image is never uploaded to any server — all recognition runs locally in your browser. Copy or download the recognized text as a plain text file.
- Upload your image (JPG, PNG, WebP, or any common format) using the file picker or drag-and-drop.
- Select the language of the text in the image for best accuracy.
- Recognition begins automatically — a progress bar shows the OCR status.
- Review the extracted text in the editable text area and correct any errors.
- Copy the text to your clipboard or download it as a .txt file.
Your data never leaves your device — 100% private processing.
How OCR works: from pixels to text
Tesseract.js (the JavaScript port of Google's Tesseract OCR engine) processes the image through several stages: binarization (converting to black-and-white for sharper character boundaries), layout analysis (detecting text regions, paragraphs, and lines), character segmentation (isolating individual characters), and neural-network recognition (matching character shapes to known glyphs). For printed text at 150+ DPI, Tesseract achieves high accuracy. Handwriting, decorative fonts, and very small text reduce accuracy. For best results, use clear, high-contrast images of printed text on a plain background at a resolution of at least 100 DPI.
Language selection and recognition accuracy
Tesseract uses language-specific trained data files to recognise characters, ligatures, and common word patterns for each script. Selecting the correct language dramatically improves accuracy — misidentifying a French document as English causes it to misread accented characters and common French words. For multi-language documents, Tesseract supports combined language codes. Printed Latin-script languages (English, French, German, Spanish) achieve the highest accuracy. Arabic and Chinese require separate trained models and may be slower to load. Handwritten text of any language is inherently less reliable than printed text for current OCR engines.
| Language family | Accuracy (printed) | Notes |
|---|---|---|
| Latin script (EN, FR, DE, ES) | Very high (95–99%) | Best supported; fastest |
| Cyrillic (Russian, etc.) | High (90–95%) | Good support |
| CJK (Chinese, Japanese, Korean) | Moderate (80–90%) | Requires larger model |
| Arabic / RTL scripts | Moderate (75–88%) | Right-to-left; specialised model |
| Handwriting (any) | Variable (50–80%) | Less reliable; use clear scans |
Glossary
- OCR
- Optical Character Recognition — the process of converting images of text into machine-readable text characters.
- Tesseract
- An open-source OCR engine originally developed by HP and maintained by Google, considered one of the most accurate open OCR engines.
- Binarization
- Converting a grayscale image to pure black and white to improve character boundary detection in OCR preprocessing.
- DPI
- Dots Per Inch — a measure of image resolution; higher DPI improves OCR accuracy for scanned documents (150+ DPI recommended).
Related reading
Frequently Asked Questions
Why use Image to Text (OCR)?
- On-device Tesseract OCR processes your image without sending pixels to a remote server
- Language selector boosts recognition accuracy for non-English and multi-script documents
- Editable output area lets you correct mis-recognised characters before copying or saving
- Handles screenshots, scanned receipts, and photographed signage without a scanner driver
Common use cases
- Extract the text from a screenshot of an error message to paste into a search engine query
- Digitise a printed recipe or article clipping by photographing it and running OCR
- Pull address and contact details from a scanned business card into plain text
- Convert photographed whiteboard notes into editable text after a brainstorming session
- Extract tabular data from a printed financial statement scan to paste into a spreadsheet
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