Image to Text - Free Online OCR Tool

How to Extract Text from Images

Upload an image by tapping the upload area or dragging a file onto it. On mobile devices, you can take a photo directly using your camera. Select the language of the text in your image, then click Extract Text. The OCR engine will process your image and display the recognized text in an editable text area that you can copy or download.

For best results, make sure your image has clear, legible text with good contrast between the text and background. Straight, well-aligned text produces higher accuracy than skewed or rotated images.

What Is OCR and How Does It Work

Optical Character Recognition is the technology that converts images of text into machine-readable characters. This tool uses Tesseract.js, an open-source OCR engine originally developed by HP and now maintained by Google, compiled to WebAssembly so it runs entirely in your browser.

The recognition process involves several stages. First, the image is preprocessed to improve contrast and identify text regions. Then the engine segments the image into individual lines, words, and characters. Each character is compared against trained models that have learned to recognize letterforms across dozens of fonts and styles. Finally, a language model helps resolve ambiguous characters by considering the context of surrounding words.

Browser-Based Processing for Privacy

Unlike server-based OCR services, this tool processes everything locally. When you upload an image, it stays on your device. The Tesseract.js WebAssembly engine downloads the language model data once and caches it in your browser. Subsequent uses are faster because the model is already available. No account is needed, no data is collected, and no images are transmitted over the network.

Common Use Cases for Image to Text Conversion

Receipts and Invoices

Photograph paper receipts to extract transaction details, dates, and amounts. This is useful for expense tracking, accounting, and tax preparation. For numeric-heavy documents like receipts, try the whitelist characters option in advanced settings with digits and common punctuation to improve accuracy.

Business Cards

Snap a photo of a business card to quickly extract contact information including names, phone numbers, email addresses, and company details. This eliminates manual data entry and reduces the chance of transcription errors.

Screenshots and Digital Images

Extract text from screenshots of websites, apps, error messages, or chat conversations. This is particularly useful when you need to copy text from an image-based PDF, a locked application, or a video frame.

Whiteboard and Meeting Notes

Capture handwritten notes from whiteboards, flip charts, or notebooks. While OCR accuracy is lower for handwriting than for printed text, clear handwriting with good contrast often produces usable results that save significant typing time.

Book Pages and Documents

Digitize text from physical books, printed documents, or magazine articles. Position the page flat with even lighting for the best results. Avoid shadows across the text and ensure the entire page is within the frame.

Multilingual Documents

With support for 13 languages including Chinese, Japanese, Korean, and Arabic, you can extract text from documents in multiple scripts. Select the appropriate language before processing to ensure the correct character recognition models are used.

Tips for Better OCR Results

Getting accurate text extraction depends largely on image quality. Here are practical tips to improve your results.

Lighting matters. Ensure even, diffuse lighting across the entire document. Avoid harsh shadows or bright spots that obscure text. Natural daylight or overhead lighting works best for photographing documents.

Keep it straight. Align the camera parallel to the document surface. Skewed or perspective-distorted text is harder for the OCR engine to process accurately. Many phone cameras have a grid overlay feature that helps with alignment.

Resolution helps. Higher resolution images contain more detail for the engine to work with. A minimum of 300 DPI equivalent is recommended for printed text. Avoid zooming in digitally after capture, as this adds blur without adding detail.

Contrast is key. Dark text on a light background produces the best results. If your source document has low contrast, try the grayscale preprocessing option in advanced settings, which can help the engine distinguish text from background.

Choose the right language. Always select the correct language before processing. The language model significantly influences character recognition accuracy, especially for non-Latin scripts like Chinese, Japanese, Korean, and Arabic.

Use advanced options when needed. For specialized content like serial numbers, license plates, or numerical data, use the character whitelist feature to restrict recognition to expected characters. For single-line text or isolated words, change the page segmentation mode to match your content layout.

Supported Languages and Scripts

This tool supports Latin-based scripts (English, Spanish, French, German, Portuguese, Italian, Polish, Dutch), Cyrillic script (Russian), CJK characters (Chinese Simplified, Japanese, Korean), and Arabic script. Each language uses a dedicated trained model optimized for that script’s character set and common word patterns. The language models are downloaded on demand and cached in your browser for subsequent use.