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Image to Text (OCR)

Upload an image to recognize and extract its text with on-device OCR — copy the result with no files ever leaving your browser.

Files never leave your browser
Files never leave your browser

Drop an image here or click to upload

JPG, PNG, WebP · Max 25 MB

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.

  1. Upload your image (JPG, PNG, WebP, or any common format) using the file picker or drag-and-drop.
  2. Select the language of the text in the image for best accuracy.
  3. Recognition begins automatically — a progress bar shows the OCR status.
  4. Review the extracted text in the editable text area and correct any errors.
  5. 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 familyAccuracy (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 scriptsModerate (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).

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Frequently Asked Questions

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Why use Image to Text (OCR)?

  • Lossless and lossy compression options to balance quality vs file size
  • Supports all major formats: JPEG, PNG, WebP, AVIF, GIF
  • Batch operations keep filenames and folder structure intact
  • Runs client-side — no image data ever leaves your device

Common use cases

  • Resize product photos before uploading to an online store
  • Compress images to pass file-size limits on job application portals
  • Convert PNG screenshots to WebP for faster web pages
  • Create thumbnails for YouTube or social media posts
  • Remove backgrounds from profile photos

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