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Yes. Browser-side OCR can read a receipt image on your device without sending the image to a remote OCR service—but using an OCR engine in a browser does not, by itself, prove that an app never transmits the image. That stronger privacy claim depends on the exact application and its network behavior.
How browser-side OCR can read a receipt locally
A web page can receive a file you select and process it in the browser. Tesseract.js is one option: its project says it brings the Tesseract OCR engine to browsers and Node.js by wrapping a WebAssembly port. Its API accepts browser File and Blob inputs, so a page can pass a locally selected or dropped receipt image to the OCR engine without first uploading that image to an OCR server. Tesseract.js project documentation
The word “can” matters. A page that performs recognition locally may still make other network requests, and local file access is not proof that the page keeps the file local.
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To support that claim, the particular deployed app must avoid transmitting the receipt image—not merely run OCR in the browser. Browser file APIs let an application read a user-selected file, while web applications can also send data over the network. MDN: File
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Moving OCR into a Web Worker can keep intensive work away from the page’s main UI thread, but a worker is not a privacy boundary. MDN documents that workers can make network requests using APIs such as fetch() and XMLHttpRequest. MDN: Using Web Workers
A credible “never leaves the device” assurance therefore needs to apply to the complete app and the exact build: its code, dependencies, and actual network behavior. An OCR engine running locally is evidence of a possible implementation, not verification of that broader promise.
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How to evaluate a browser receipt OCR app
- Check where recognition runs. Look for documentation identifying a client-side OCR engine, such as Tesseract.js and its WebAssembly implementation. This establishes a local-processing option, not a no-transmission guarantee.
- Check what the app says it sends. Look for a specific explanation of whether receipt images, extracted text, or other data leave the device. Treat privacy language as the provider’s claim unless the exact implementation has been independently verified.
- Verify the deployed app’s network behavior. For a strong assurance, examine the complete code and dependencies and check network activity for the exact deployed build while processing a receipt. A source-code description or a worker-based design alone cannot establish that no image transmission occurs.
- Confirm format support. Make sure the OCR tool accepts the file type you plan to use; support for images does not imply support for PDFs.
- Evaluate results on representative receipts. Recognition quality and responsiveness should be assessed for the kinds of receipts and devices in your workflow. The cited project documentation does not provide a receipt-specific accuracy or speed comparison.
Which receipt files Tesseract.js documents
Tesseract.js documents image inputs including BMP, JPEG, PNG, PBM, WebP, and non-animated GIF. Its browser input types include File and Blob. The project says it does not support PDF files; a PDF-based workflow would need a separate PDF-capable tool or a conversion step. Tesseract.js image-format documentation
The project also states that Tesseract.js does not modify Tesseract’s recognition model to improve accuracy. Its documentation establishes browser feasibility and format scope, not how accurately it will read a particular receipt. Tesseract.js project documentation
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What a vendor’s privacy promise establishes
OOCR’s privacy page, last updated 2026-06-28, says, “Your images never leave your device” and “There is no upload path.” Those are OOCR’s claims about its own PP-OCRv6 WebAssembly service; they are not evidence about another app, and the statements alone are not an independent audit. OOCR privacy page
For any service, distinguish three things: the engine’s ability to run locally, the provider’s stated handling of files, and verification of the deployed app’s actual network behavior. They answer different questions.
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