Its much lower and the error rate is much higher. It works, but for our usecase, "clean rendered text" (extract from kindle for example) tesseract is much better.

We are still waiting for the firefox extension to be approved, i will post it here whenever we hear something. In the meanwhile it can be tested with the zip file here https://github.com/thiagotigaz/ocr-it/releases or by building manually.

Until it's approved you guys can download the ready to use releases:

Download ocr-it-firefox-0.3.0.zip from https://github.com/thiagotigaz/ocr-it/releases/tag/v0.3.0

If you'd rather build from source, the steps are in the README: https://github.com/thiagotigaz/ocr-it#install

For the input text rendered on screen, Tesseract did better on both accuracy and speed. We got about 0.1% character error vs 1–2% for RapidOCR, and Tesseract was roughly 2.5x faster. Blur was the biggest difference: 0.4% vs 14%.

The big problem is that this is synthetic rendered text, which is basically the easy case and also the only kind of input this extension captures. I wouldn't assume the same results for scanned documents.

I haven't tested EasyOCR yet.

OCR It is a Chrome extension for that gap. You drag out a capture region once — the text block of the reader, say. After that, one hotkey per page screenshots that exact rectangle, OCRs it, and appends the result to a running transcript. Or start an auto-run and it captures, turns the page, and repeats until the document ends. Then Copy all, or Download .txt, and you have a file to paste into Claude or drop into an agent's context.

Everything runs locally. Tesseract's wasm build and the language data (~10 MB) are committed into the extension, so there are no network requests at all, no API key, and no host permissions at install — single captures ride on activeTab. The irony of an AI-adjacent tool that never talks to a server was not lost on me, but the pages you're capturing are often exactly the ones you don't want to ship to a third party.

Three things turned out more interesting than expected:

- MV3 service workers have no DOM and no Worker, so cropping and OCR live in an offscreen document.

- The next-page control is stored as a point, not a CSS selector. A point survives DOM re-renders and reaches into cross-origin iframes and shadow roots, which nothing the top frame can express does. Routing it was the fiddly part: window.screenX inside an iframe reports the browser window, not the frame, so frames locate themselves by walking same-origin ancestors, and across an origin boundary the parent hands the offset down by postMessage.

- The auto-run waits for each page's OCR before turning. That's what makes end-of-document detection work; a timer-based loop sails past the last page and fills your transcript with copies of it.

Limitations: Chrome's own PDF viewer can't be auto-advanced (it's a plugin no extension can inject into, though capturing from it works fine); the region is a fixed rectangle on screen, so resizing or zooming mid-run breaks it; and accuracy tracks the source — crisp rendered text reads at 93-95% confidence, scans need cleanup before they're worth feeding to anything.

Tests drive a real headless Chrome over CDP, which had its own surprises: Chrome 137+ ignores --load-extension, and headless can't show the optional-permission prompt, so the suite installs a copy with the grant baked in plus a real toolbar click via Extensions.triggerAction to prove the ungranted path still works.

MIT, no build step: https://github.com/thiagotigaz/ocr-it

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