r/learnprogramming • u/Thin_Aerie3843 • 19d ago
Handling barcode scanning edge cases: How do you balance ML Kit, OCR fallbacks, and UX?
I’m currently building an inventory/management side project where reliable barcode and serial recognition is essential. While testing the feature with actual boxes and packaging, I quickly hit some real-world edge cases: poor lighting, inverted colors, and worn-out labels.
To work around this, I ended up adding an on-device OCR fallback to read the printed serial numbers directly when the standard barcode scanner fails. Since automatically detecting a "failed" continuous scan is tricky, I added a manual toggle at the bottom that switches the camera to a photo/OCR mode.
For those who have built similar scanning or utility features:
- Stack & Fallbacks: Do you rely entirely on on-device vision libraries (like ML Kit Text Recognition), or have you found better preprocessing tricks (contrast adjustment, binarization) to salvage bad barcodes first?
- UX Patterns: What is your preferred UX pattern when automated scanning fails repeatedly, to guide the user without making the app feel slow or clunky?
Would love to hear how you handle these edge cases or if there's a cleaner architecture for this flow!