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I think the difficulty was specifically with CAPTCHA challenges, which had to be quick to generate but still legible - OCR on physical documents has to be robus

I think the difficulty was specifically with CAPTCHA challenges, which had to be quick to generate but still legible - OCR on physical documents has to be robust to a different set of problems That said, what kind of errors are you getting? I think the main practical difference between Tesseract's LSTM-based OCR and newer VLM-based OCR like PaddleOCR [1] is (hopefully) getting to skip making heuristics for the layout of the document, but errors possibly compounding over multiple tokens - are you getting individual illegible words or having the documents smooshed together because the OCR can't parse the layout? [1] https://github.com/PaddlePaddle/PaddleOCR https://github.com/PaddlePaddle/PaddleOCR

no-name-here 27d ago
Good points. >> no matter how I scan receipts or documents, the OCR output seems far worse than human > what kind of errors are you getting? Just noticeably worse character recognition, particularly where the document is faded, water-stained, or the paper document (not the scan) was low resolution to begin with, as compared to 'normal' human recognition. I was largely using Tesseract in conjunction with the self-hosted Paperless-NGX, and I wanted to stay free/local without yet investing in AI-focused hardware. But you're right that AI will continue to advance, including in smaller local models, and it looks like Paperless 3.0 released recently (after my testing earlier in 2026), including with AI functionality. > or having the documents smooshed together because the OCR can't parse the layout I haven't even been worrying about that yet - I'm just at the point of trying to get the OCR characters right. :-)