Hi,
A bit of a late reply, but still – here are a few thoughts on the approach in general. To be clear, I am referring to the underlying problem in the stores rather than the Datathon case. My feeling is that this is a bit of an XY problem (https://en.wikipedia.org/wiki/XY_problem), and you have it a bit backwards 😊
If I were to formulate the business issue, I’d go like this:

1. Make a “map” of the store, with all shelves and expected product placements. Not really sure how much this would cost but considering many autonomous driving approaches involve building centimeter-level-accurate maps of whole cities, it should be feasible. See below for a feasible simplification.

2. Then the problem would be to match: [This is a place where somebody would mention Bayesian priors, I guess?]
2a. Whether the product seen in the image matches the expected product (which I think is an easier task than identifying the product from scratch). And you can get extra info — is the product placed properly (front side facing the front and fully visible, etc.). This is lower priority but you might still want it fixed.
2b. Whether the label is what it is expected to be.

3. Currently, a lot of the sample photos seem to be adjacent to each other, with substantial (in some cases) overlap. Go further – combine the images into a ‘panoramic’ image that would ideally capture a whole shelf’s length. That would help greatly in (1) above – you do not need full coordinates in space of all shelves/products, a “map” would be something like “On shelf A, products are expected to be in the following order – X, Y, Z…”

4. Lastly, labels:
4a. Yes, labels can be made to be “machine-readable” with minimal effort. Lots of options to choose from – proper fonts (there are lots of questions on this on Stack Overflow/Quora/etc.); barcodes, QR codes…
4b. Another option – so that a bigger barcode or QR code, or uglier font do not stick out in a bad way… Make the QR codes “open”, there are many ways this would provide value to the customer. E.g. I can have my phone remember what I buy typically (and maybe next time notify me on which shelf to find it; provide extra information on the product – nutrition values for food, etc.; get notified of promotional prices; …)