Image to Text OCR

About Image to Text OCR

Image to Text OCR uses Tesseract.js v5 to perform full optical character recognition entirely in your browser — no image is ever uploaded to a server. Simply upload a scanned document, screenshot, photo of printed text, or any image containing readable characters. Choose from 10 supported languages, click Extract Text, and receive the recognized content in a copyable textarea. A confidence score tells you how reliably the engine read the image. You can copy the result or download it as a plain .txt file.

Why use Image to Text OCR

• Runs 100% in-browser — no file uploads, full privacy.
• Supports 10 languages including CJK, Arabic, and Hindi.
• Confidence score helps you gauge extraction quality at a glance.
• Download output as .txt for use in any application.
• Free, no sign-up, no watermarks, works on mobile.
• Runs 100% in-browser via Tesseract.js compiled to WebAssembly — no file uploads, full privacy.

How to use Image to Text OCR

1. Upload an image containing text (JPG, PNG, GIF, WebP).
2. Select the language that matches the text in the image from the dropdown.
3. Optionally choose whether to preserve or strip extra line breaks.
4. Click 'Extract Text' and watch the progress bar as OCR runs.
5. Review the extracted text in the output area and check the confidence score.
6. Click 'Copy Text' or 'Download .txt' to save the result.
7. Upload an image containing text (JPG, PNG, GIF, WebP, BMP).

When to use Image to Text OCR

• Digitizing printed documents, receipts, or business cards.
• Extracting text from screenshots or screen recordings.
• Converting scanned book pages or PDFs to editable text.
• Pulling subtitles or captions from video frame screenshots.
• Digitizing printed documents, receipts, business cards, or product labels into editable text.
• Extracting text from screenshots, screen recordings, or social media images.

Examples

Tips

• Crop tightly around the text region before uploading — Tesseract performs better on clean text-only images than on cluttered photos with lots of background.
• If the confidence score is below 70, try increasing image resolution (300 DPI is the OCR sweet spot) or improving contrast before re-running.
• For multi-language documents, run OCR once per language and merge the results manually — Tesseract.js loads one language model at a time.
• Skewed or rotated text reduces accuracy; rotate the image to upright before running OCR.
• Photographs of glossy receipts often have glare; matte them with a dark cloth and reshoot for a much cleaner read.
• If your image is a screenshot of a webpage, prefer copying the original text directly when possible — OCR is a fallback for content you cannot select.

Frequently Asked Questions

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Glossary

OCR

Optical Character Recognition — the process of detecting and decoding printed or handwritten characters from an image into machine-readable text.

Tesseract

An open-source OCR engine originally developed at HP and now maintained by Google; widely regarded as the most accurate free OCR engine.

Tesseract.js

A pure JavaScript / WebAssembly port of Tesseract that runs entirely in the browser without a server backend.

WebAssembly (Wasm)

A portable bytecode format that lets compiled C/C++ code run at near-native speed inside the browser; Tesseract.js uses it for the OCR engine.

Language pack

A trained data file (.traineddata) containing the character shapes, language model, and dictionary for a specific language; each pack is 10–25 MB.

Confidence score

A 0–100 value reported by the OCR engine indicating how sure it is about each recognised word; lower scores often mean low resolution, glare, or unusual fonts.

DPI

Dots per inch — a measure of scan resolution. 300 DPI is the standard sweet spot for OCR; below 150 DPI accuracy drops sharply.