Convert Image to Text Online
Extract editable, selectable text from screenshots, scanned documents, receipts, and invoices in seconds. Powered by self-hosted browser WebAssembly OCR, your images and extracted text remain 100% private on your device—with zero server uploads.
Select an image or drag & drop here
Extract editable text from JPG, PNG, or WebP · Up to 10 MB and 20 MP
Review Image
Verify your image before starting optical character recognition.
Reading text from image...
Preparing OCR engine...
Text Extracted Successfully
Extracted from image.png
Unable to extract text
OCR Cancelled
The text extraction process was cancelled. The tool will clean up the OCR worker when it is available.
How to Extract Text from Images Online
Select or Drop Image
Choose a JPG, PNG, or WebP image from your computer, phone, or clipboard. Files up to 10 MB and 20 megapixels load directly into local browser memory.
Extract with WebAssembly OCR
Click Extract Text. The local Tesseract LSTM engine analyzes character contours, words, and paragraph spacing entirely on your device's CPU.
Edit, Copy, or Download
Review the extracted text in the live editor. Correct any typos, copy the text to your clipboard with one click, or download a clean UTF-8 .txt file.
Screenshot to Text, Document Scanning, and More
Optical character recognition turns non-selectable visual text into editable digital information across everyday workflows.
Screenshot to Text
Quickly grab code snippets, error messages, URLs, presentation slides, or chat messages from screenshots without retyping character by character.
Invoices & Receipts
Extract vendor names, line items, transaction numbers, and totals from expense receipts and invoices for bookkeeping and expense reporting.
Scanned Documents & Letters
Convert photo scans of printed letters, agreements, business cards, and book pages into plain text ready for word processors and archiving.
Confidential & Legal Data
Extract text from sensitive contracts, medical summaries, and financial records safely. Because processing is 100% local, no third party ever sees your content.
Why Browser-Local OCR is Safer for Your Data
Images submitted to OCR converters often contain personal names, addresses, banking details, or proprietary business information. Traditional converters send your files to remote cloud APIs. ToolRipple runs entirely on your machine.
Zero Server Uploads
Your images and recognized text never leave your device. WebAssembly decodes image buffers and runs neural network recognition strictly inside your browser's private memory sandbox.
Self-Hosted Open-Source Assets
All WebAssembly binaries, worker scripts, and English traineddata models are self-hosted directly from ToolRipple. There are zero tracking beacons, telemetry pings, or third-party CDN requests.
Warm Worker Reuse
The OCR engine initializes once and stays warm in background memory during your session, allowing instant repeat extractions without redownloading models or restarting decoders.
Local Processing and Cleanup
When you start over, the tool clears the selected image and extracted text from the interface. Browser memory is reclaimed on its own schedule. The OCR engine may cache its English language model in browser storage; your image and extracted text are not intentionally saved there.
Technical Limits & Supported Formats
Engineered with transparent boundaries for consistent browser stability, speed, and privacy.
| Parameter | Specification & Behavior |
|---|---|
| Input Formats | JPG, JPEG (.jpg, .jpeg), PNG (.png), and static WebP (.webp) |
| File Count Limit | Exactly 1 image per OCR operation (ensures optimal focus and memory stability) |
| File Size Limit | Up to 10 MB per image |
| Resolution Limit | Up to 20 megapixels (e.g. 5000 × 4000 px), verified via image binary headers |
| Unsupported Formats | PDF documents, GIF animations, and Apple HEIC photos. Convert these to a supported image format before using OCR; ToolRipple's HEIC converter is not yet available. |
| Language Support | English (eng) using pre-trained LSTM neural network models |
| OCR Engine | Tesseract.js v6.0.1 WebAssembly engine (Apache-2.0 license) |
| Asset Hosting | 100% self-hosted on ToolRipple static storage (/vendor/tesseract/) · 0 CDN calls |
| Export Formats | Editable plain text, one-click system clipboard copy, and UTF-8 .txt download |
| Network Activity | 0 bytes uploaded — 100% client-side memory execution |
How Optical Character Recognition Works & How to Get High Accuracy
Optical Character Recognition (OCR) is the process of translating digital pixels in an image into computer-readable, selectable text characters. Understanding how the browser-based neural network analyzes images helps you achieve the highest recognition accuracy:
1. Preprocessing & Binarization
The OCR engine first converts color pixels to grayscale and establishes a high-contrast threshold (binarization). Dark text characters are separated from lighter background paper, banners, or digital canvases.
2. Line & Word Segmentation
The engine identifies horizontal reading baselines, grouping connected pixel clusters into discrete lines and detecting whitespace gaps to separate individual words.
3. Neural Feature Extraction
Using a Long Short-Term Memory (LSTM) recurrent neural network, the engine compares character contours against trained linguistic patterns, evaluating preceding and following characters to assign confidence scores.
4. Text Normalization
Recognized character streams are normalized: Windows CRLF line breaks become clean standard newlines, invisible control characters are filtered out, and word spacing is preserved for clear editing.
Best Practices for Maximum OCR Accuracy
- Ensure High Contrast: Black text on a clean white background achieves near-perfect recognition. Avoid dark text over colored graphics or faint gray text on off-white backgrounds.
- Avoid Blurry or Low-Resolution Images: Small screenshots (under 300 pixels wide) often lack sufficient pixel density for the neural network to differentiate similar letters like 'e', 'c', and 'o'.
- Orient Text Horizontally: The OCR engine reads horizontally from left to right. Rotate sideways or upside-down photos before uploading so text lines are level.
- Standard Printed Fonts Work Best: Clean sans-serif and serif typefaces (Arial, Helvetica, Times, Inter, Roboto) yield the highest accuracy. Ornate cursive scripts, calligraphy, and handwritten notes have significantly lower recognition rates.
Common Recognition Issues and Solutions
Practical ways to resolve unexpected OCR results and achieve clean text extraction.
Similar Character Substitution (0 vs O, 1 vs l)
In certain fonts, a numeral '0' looks almost identical to uppercase 'O', and numeral '1' resembles lowercase 'l' or capital 'I'. ToolRipple's editable textarea lets you quickly scan and correct these minor substitutions before exporting.
Garbled Characters from Low-Light Photos
Smartphone camera shadows or uneven glare across paper can introduce pixel noise that the neural net mistakes for punctuation marks. Flatten the document under even lighting or increase contrast before taking the photo.
No Text Detected (Empty Result)
If the image contains only non-text drawings, complex logos, or extremely low contrast, the engine may legitimately find 0 readable characters. ToolRipple flags empty extractions immediately with helpful guidance.
Multi-Column Layout Reading Order
Newspaper or magazine clippings with multiple vertical columns may sometimes have text lines merged across columns. In such cases, crop or screenshot one column at a time for orderly paragraph flow.
Frequently Asked Questions
Are my images or extracted text uploaded to ToolRipple servers?
No. ToolRipple operates on a strict zero-upload architecture. Optical character recognition executes directly inside your browser using a self-hosted WebAssembly engine. No image files, pixel buffers, or extracted text are ever transmitted to any remote server or external API.
What image formats and file sizes are supported?
ToolRipple accepts JPG, JPEG, PNG, and static WebP images up to 10 MB and 20 megapixels per image. Animated WebP images, GIF files, and PDF documents are not supported.
Can I extract text from a PDF file?
Not directly. ToolRipple V1 specializes in raster images (JPG, PNG, WebP). To extract text from a PDF, take a screenshot or export the PDF page as an image before loading it into the Image to Text converter.
Why did the tool return empty text or low accuracy?
OCR accuracy depends heavily on source image clarity, contrast, and font styling. If an image is blurry, low resolution, poorly lit, or contains stylized handwriting, the OCR engine may struggle or return blank output. Use sharp, high-contrast images with clearly printed English text for best results.
Can I edit the text before downloading or copying?
Yes. The extracted text is displayed in a fully editable textarea. You can review, correct typos, adjust formatting, or remove unwanted lines. The Copy Text and Download TXT buttons always use your current edited text.
What language does the OCR tool support?
ToolRipple currently supports English language OCR (eng) using an optimized LSTM neural network model. Multi-language recognition will be evaluated in future releases.
Open-Source Technology & Compliance Notice
ToolRipple uses the open-source Tesseract.js (v6.0.1) WebAssembly engine and pre-trained English language models, distributed under the Apache License 2.0. All recognition models and WebAssembly decoders are self-hosted directly on ToolRipple static infrastructure under /vendor/tesseract/ with zero runtime requests to external CDNs.
Apache 2.0 License · Tesseract.js Source Code · Tesseract OCR Engine