I’ve been working on a mobile checkout feature where users can add their bank card by scanning instead of typing everything manually. In testing it works fine, but in real usage it becomes inconsistent very quickly—glare, motion blur, different phone cameras, and slightly damaged cards all affect results. I’m trying to understand if this is just a limitation of standard OCR or if AI-based solutions handle it significantly better in production. I also found an example of a scan credit card with phone that seems to focus on structured AI extraction rather than simple text recognition: https://ocrstudio.ai/bank-card-scanner/ . Has anyone actually used something like this in a real app and can share how stable it is under real-world conditions?
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I’m not in fintech, but I’ve seen similar behavior in document scanning and ticket validation apps. Everything looks simple in demos, but real users introduce unpredictable conditions like lighting, movement, and device differences. It’s interesting how most modern apps are no longer trying to achieve “perfect recognition,” but instead focus on recovering smoothly when recognition is imperfect.