Image to Text OCR
Run local OCR in your browser and copy or download editable extracted text.
OCR runs locally in your browser. Images and extracted text are not uploaded to UtilityStacks servers.
OCR runs in your browser. Your image and extracted text are never uploaded to UtilityStacks servers.
OCR options
Extracted text
Use in code
Client-side OCR can run fully in-browser with OCR workers, image preprocessing, and local text export.
Extract image text with Tesseract.js
TypeScriptPrimary API
createWorker(language).recognize(image)
Creates an OCR worker, recognizes text from an image Blob, and returns extracted text.
Install
import { createWorker } from "tesseract.js";
async function ocrFromImage(image: Blob) {
const worker = await createWorker("eng", 1, {
logger: (m) => console.log(m.status, m.progress),
});
try {
const { data } = await worker.recognize(image);
return data.text;
} finally {
await worker.terminate();
}
}Reference: tesseract.js documentation
How to use
- Upload an image or drag and drop it into the OCR input area.
- Choose language and preprocessing settings such as rotation, crop, and contrast.
- Run OCR, review editable text, then copy or download the output.
Use cases
- Extract text from screenshots, receipts, notes, and scanned documents.
- Convert printed paragraphs into editable draft text.
- Capture text snippets from design mockups and diagrams for documentation.
Limitations and caveats
- OCR accuracy depends on image clarity, orientation, and font quality.
- Handwriting and stylized fonts can produce lower-quality extraction.
- Large multilingual documents may run slower due to browser memory and CPU limits.
History
- Optical character recognition started with early pattern-matching systems for machine-printed text.
- Modern OCR adopted machine learning to improve recognition across noisy documents and diverse fonts.
- Web-based OCR evolved as browsers gained stronger canvas, file, and WebAssembly capabilities.
Source: Tesseract.js repository 路 MDN Canvas API 路 MDN File API
Evolution and improvements
- OCR workflows moved from server-only pipelines to privacy-focused browser execution.
- Recent engines support on-demand language loading and incremental recognition progress updates.
- Frontend tooling now combines preprocessing controls with local OCR to improve extraction quality.
Source: Tesseract.js repository 路 MDN Canvas API 路 MDN File API
FAQ
Does this upload my image to a server?
No. The OCR workflow runs in your browser session. UtilityStacks does not upload image or extracted text content.
Why can OCR fail on some images?
Low contrast, blurry capture, unusual fonts, or heavy background noise can reduce recognition quality.
Should I use threshold and contrast options?
Yes, especially for low-contrast scans. Preprocessing can materially improve text detection before OCR runs.