Image to Text (OCR)
Extract text from any image using optical character recognition
Drop an image here or click to upload
JPEG, PNG, WebP, screenshots — max 20 MB
🔒 Files never leave your browser. OCR runs locally using Tesseract.js — nothing is uploaded.
What Is OCR? How Image-to-Text Works
OCR — Optical Character Recognition — is a technology that converts images of typed, handwritten, or printed text into machine-readable characters. When you photograph a document, take a screenshot of an invoice, or scan a page from a book, what you have is a raster image: a grid of colored pixels. OCR software analyzes those pixels, recognizes patterns that correspond to letters and digits, and outputs a string of text that a computer can process, search, copy, and edit.
This tool uses Tesseract.js — the JavaScript port of Google's Tesseract, one of the most accurate open-source OCR engines in the world. Originally developed at HP Labs in the 1980s and open-sourced by Google in 2005, Tesseract has been continuously improved and now supports over 100 languages. Tesseract.js compiles Tesseract to WebAssembly, allowing it to run directly in your browser without any server-side processing.
The OCR process involves several stages: image preprocessing (binarization, deskewing, noise removal), layout analysis (identifying text blocks, lines, and words), character recognition (matching pixel patterns to known glyph shapes using machine learning models), and post-processing (applying language models to improve accuracy based on word frequency and context).
Practical Uses for Online OCR
Digitizing physical documents. Receipts, business cards, handwritten notes, printed reports, and old paper documents can all be digitized with OCR. Instead of manually retyping content, you photograph the document and extract the text in seconds.
Making scanned PDFs searchable. PDFs created from scans are images — they cannot be selected or searched. Running OCR on a page screenshot gives you searchable, copyable text from those documents without needing Adobe Acrobat Pro or other expensive software.
Extracting text from screenshots. When you receive a screenshot with text you need to copy — an error message, a design mockup, a social media post, or a code snippet embedded in an image — OCR lets you extract and use that text immediately.
Accessibility. Converting images of text to actual text makes the content accessible to screen readers, translation tools, and text-to-speech software for users with visual impairments.
Data extraction and research. Extracting tabular data from images of spreadsheets, financial statements, or reports for further analysis. Researchers digitizing historical documents or newspaper archives use OCR extensively.
How to Get the Best OCR Accuracy
OCR accuracy is primarily determined by image quality. Following these guidelines consistently produces much better results:
Resolution matters most.Text should be large enough in the image for the OCR engine to distinguish individual character details. A minimum of 300 DPI (dots per inch) for scanned documents is the industry standard. For screenshots, make sure you are capturing at your screen's native resolution and not a scaled-down version.
High contrast is essential. Black or very dark text on a white or light background produces the highest accuracy. OCR struggles with colored text on colored backgrounds, watermarked text, or text with low contrast. If possible, convert the image to greyscale or increase contrast before processing.
Text must be horizontal. Tesseract works best with horizontally aligned text. Rotated documents, perspective-distorted photos (taken at an angle), or curved text (on book spines or product packaging) produce lower accuracy. Modern Tesseract includes some automatic deskewing, but the improvement is limited.
Choose the correct language.Tesseract uses statistical language models to improve recognition accuracy based on word frequency and patterns in the selected language. Selecting the wrong language (for example, "English" for a French document) produces worse results even if the underlying alphabet is the same, because the language model will not recognize common French words.
Understanding the Confidence Score
After OCR completes, this tool shows a confidence score as a percentage. This score represents Tesseract's average certainty across all recognized characters and words in the image. A higher score means the engine was more certain about its recognition decisions.
As a general guide: scores above 80% typically produce very accurate text with few errors; 60–80% produces mostly correct text with some substitution errors (for example, '0' vs 'O', 'l' vs '1'); below 60% indicates the OCR struggled significantly and the output should be carefully proofread.
The confidence score is an average — it does not tell you which specific words or characters were uncertain. Always review the output, especially when the score is below 80% or when the text contains numbers, punctuation, or technical terminology that may not appear frequently in Tesseract's language model training data.
Why Browser-Based OCR Is Better for Privacy
Many documents that benefit from OCR are inherently sensitive: financial statements, medical records, legal contracts, personal identification documents, and proprietary business data. Uploading these to a third-party OCR server creates real privacy and compliance risks.
This tool processes your images entirely locally using Tesseract.js compiled to WebAssembly. Your image data never leaves your device. There is no server that receives, processes, or logs your documents. GDPR, HIPAA, and other privacy regulations that restrict the sharing of personal data are not implicated because no data is shared with any external party.
You can confirm this behavior by disconnecting from the internet after loading the page — once the language data is downloaded and cached, OCR continues to work perfectly offline.