Image to Text (OCR)
Extract editable text from screenshots, scanned documents, receipts, and photos using Tesseract.js — the industry-standard open-source OCR engine. Supports 15+ languages including English, French, German, Spanish, Chinese, Japanese, and Arabic. All processing runs locally in your browser with no uploads and no signup required.
Upload an image and extract all readable text using Tesseract.js OCR — runs entirely in your browser. Supports 15+ languages. No uploads, no account, no server processing.
Tips for better OCR accuracy
- • Use high-resolution images with sharp, clear text
- • Ensure good contrast between text and background
- • Crop to the text area before uploading for best results
- • Select the correct document language for improved recognition
- • Avoid heavily stylized or handwritten fonts
Why Use Our Image to Text OCR?
Instant Image to Text Extraction
The image to text OCR tool extracts all readable text from your image in seconds using Tesseract.js — a battle-tested OCR engine running entirely in your browser with no server round-trips.
Secure Image to Text Online
Your image never leaves your device. The image to text OCR processes everything locally in your browser, making it safe for invoices, IDs, contracts, and other sensitive documents.
Image to Text Online — 15+ Languages
Extract text from images in English, French, German, Spanish, Portuguese, Italian, Russian, Chinese, Japanese, Korean, Arabic, Hindi, and more — all with one click, no installation needed.
100% Free Forever
Use this image to text OCR tool without signup, subscriptions, rate limits, or hidden paywalls. Extract text from as many images as you need, completely free with no ads.
Common Use Cases for Image to Text OCR
Invoice and Document Digitization
Use the image to text OCR tool to extract supplier names, invoice numbers, dates, and totals from photographed bills or scanned invoices — then paste directly into accounting sheets or ERP systems.
Screenshot to Editable Text
Convert text from UI screenshots, terminal captures, error dialogs, and online lessons into editable text using the image to text converter — searchable, shareable, and ready to paste anywhere.
Student and Research Workflows
Pull text from textbook photos, lab sheets, and presentation slides with the image to text OCR tool to save time on manual retyping while preparing study notes, citations, and assignments.
Developer and QA Debugging
Extract stack traces, error messages, and log output from screenshots posted in Slack, Jira, or GitHub issues using the image to text tool — then paste into bug reports and analysis tools.
Receipt and Expense Tracking
Capture merchant names, line items, and totals from receipt photos with the image to text OCR and copy results into expense trackers, spreadsheets, or reimbursement forms for faster bookkeeping.
Legacy Document Archiving
Extract words from old scanned pages, printed reports, and physical forms using the image to text converter and convert them into searchable digital text for indexing, archival, and collaboration.
Understanding Image to Text OCR
What is image to text OCR?
Image to text OCR (Optical Character Recognition) is a technology that detects and extracts readable text from image files — converting pixels into editable characters. This image to text tool uses Tesseract.js, a WebAssembly port of Google's Tesseract OCR engine, which runs entirely in your browser without sending any data to a server. Tesseract is one of the most accurate open-source OCR engines available, supporting over 100 languages and trained on millions of document samples. The image to text OCR tool supports PNG, JPG, JPEG, WebP, BMP, TIFF, and GIF input formats.
How our image to text OCR works
- Upload your image: Drag and drop or click to browse for any image file containing text — screenshots, scanned documents, photos of signs, receipts, invoices, or any other image with readable text. Select the document language for best accuracy.
- Browser-based OCR processing:Click "Extract Text" and Tesseract.js loads the OCR engine and language data directly in your browser. All recognition happens locally on your device — your image is never uploaded to any server. A progress bar shows each stage of the recognition process.
- Copy or download the output: The extracted text appears in the output panel with a confidence score. Copy it to your clipboard with one click, or download it as a plain .txt file for use in documents, spreadsheets, translation tools, or any other workflow.
What affects image to text OCR accuracy
- Image resolution: Higher resolution images produce significantly better OCR results. Aim for at least 150 DPI for scanned documents; 300 DPI or higher is ideal for printed text.
- Text contrast: Dark text on a light background (or vice versa) gives the OCR engine the clearest signal. Low-contrast images — such as grey text on white — reduce accuracy noticeably.
- Language selection: Selecting the correct document language loads the appropriate trained data model, which significantly improves recognition of language-specific characters, diacritics, and word patterns.
- Image cropping: Cropping the image to include only the text region — removing decorative borders, logos, and background imagery — reduces noise and improves extraction quality.
Important limitations
The image to text OCR tool works best on printed or typed text. Handwritten text, heavily stylized fonts, very small text (below ~10pt equivalent), and text on complex or patterned backgrounds will produce lower accuracy results. The confidence score shown after extraction indicates how certain Tesseract is about the recognized text — scores above 80% are generally reliable, while scores below 50% suggest the image may need improvement. The first extraction may take longer as the OCR engine and language data are loaded into your browser; subsequent extractions in the same session are faster.
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Frequently Asked Questions About Image to Text OCR
An image to text OCR (Optical Character Recognition) tool detects and extracts readable text from image files, converting pixels into editable characters. Our image to text OCR uses Tesseract.js — a WebAssembly port of Google's Tesseract engine — running entirely in your browser with no server uploads required.
No. All OCR processing happens 100% locally in your browser using Tesseract.js WebAssembly. Your image never leaves your device and is never sent to any server. This makes the image to text OCR tool safe for sensitive documents like invoices, IDs, contracts, and medical records.
The image to text OCR tool supports PNG, JPG, JPEG, WebP, BMP, TIFF, and GIF image formats. For best extraction results, use high-resolution images with clear, sharp text and good contrast between the text and background.
Yes. The image to text OCR tool is 100% free with no signup required, no premium features, and no usage limits. Extract text from as many images as you need without any restrictions, hidden costs, or ads.
The image to text OCR tool supports 15+ languages including English, French, German, Spanish, Portuguese, Italian, Dutch, Polish, Russian, Chinese (Simplified and Traditional), Japanese, Korean, Arabic, and Hindi. Select the correct language before extracting for significantly better accuracy.
Use high-resolution images (150 DPI minimum, 300 DPI ideal), ensure strong contrast between text and background, crop the image to the text area before uploading, and select the correct document language. Avoid blurry, rotated, or heavily stylized fonts for best results.
The confidence score indicates how certain Tesseract.js is about the recognized text, expressed as a percentage. Scores above 80% are generally reliable. Scores below 50% suggest the image quality may be too low for accurate extraction — try a sharper, higher-contrast version of the image.
The first extraction loads the Tesseract.js OCR engine and the selected language's trained data model into your browser — this typically takes 5–15 seconds depending on your connection speed. Subsequent extractions in the same session are significantly faster as the engine is already loaded.
Tesseract.js is primarily trained on printed and typed text, so handwritten text extraction accuracy is limited. Clear, neat handwriting may produce partial results, but heavily cursive or stylized handwriting will generally not extract reliably. For handwritten text, a dedicated handwriting recognition model would be needed.