OCR Image to Text: Extract Text from Any Image Free

OCR — Optical Character Recognition — is the technology that reads text from images. It was once expensive specialised software. Today Tesseract, an open-source OCR engine originally developed by HP and now maintained by Google, runs directly in your browser via JavaScript. No installation, no upload, no cost.

What you can extract text from

• Screenshots — grab text from a software UI, error message, or notification you cannot copy
• Scanned documents — PDFs that were scanned as images rather than generated with text layers
• Photos of printed text — books, articles, menus, receipts, whiteboards
• Business cards — extract name, email, phone number without retyping
• Subtitles in screenshots — extract captions from video screenshots
• Text in foreign languages — the tool supports 8 languages including Hindi, Chinese, Arabic, and Japanese

Getting the best accuracy

OCR accuracy is heavily dependent on image quality. The following factors have the biggest impact:

• Resolution — 300 DPI or higher is the standard for accurate OCR. Screen screenshots at 2x retina resolution (144 DPI effective) usually work well. Photographs of text taken with a smartphone camera at arm's length typically work.
• Contrast — black text on white background is ideal. Low-contrast text (light grey on white, for example) reduces accuracy significantly.
• Orientation — the text should be horizontal. Rotated or skewed text reduces accuracy (though Tesseract has some deskew capability).
• Font type — clean sans-serif and serif fonts in standard weights are recognised well. Decorative, script, or highly stylised fonts have lower accuracy.
• Language selection — always select the matching language before processing. Using the wrong language model causes garbled output.

Handwriting recognition

Tesseract was designed primarily for printed text. It can recognise very neat, consistent handwriting — printed block letters with good spacing — with reasonable accuracy. Cursive handwriting, casual notes, and mixed handwriting styles will produce poor results. For handwriting recognition, a dedicated model (Google Cloud Vision, Microsoft Azure Computer Vision) produces significantly better results, but these require an API key and upload your image to their servers.

After extraction: what to do with the text

The output text is editable directly in the result panel. Common next steps: copy to clipboard for pasting, download as a .txt file for archiving, or paste into a document editor for further formatting. For structured data like tables or columns, the extracted text may need manual cleanup — OCR treats the page as a flow of words rather than preserving grid structures.