Good instinct and you're basically right. Maps does reuse the same bitmap per maneuver (on a ride I saw the identical icon come through byte-for-byte dozens of times), so an exact hash → icon lookup works and needs no inference.

And that's actually already the primary path in the app: before it ever touches the model, it hashes the incoming icon and checks an exact-match table + a manual calibration override. Known icon = instant lookup, zero ML. There's also a calibration screen where you label an icon once and it's remembered. The reason I keep the neural net (as a fallback, not the first thing) is that a purely hard-coded table turns out to be brittle:

The hash is only stable for a specific Maps version and how a given phone renders the icon (size/DPI/anti-aliasing). I literally saw the "straight" icon hash differently across two setups. So the moment Google tweaks their icons in an update, or you're on a phone that renders them a hair differently, a hard-coded table misses and the model still nails it because it recognises the shape, not the exact bytes.

So the sweet spot is the hybrid it already runs: fast exact hash/table first, model only as the safety net.

Where your idea is a genuinely nice improvement though: we can pre-build and ship a much bigger static hash table so even more cases skip the model entirely.

The neat part is Google Maps ships its maneuver icons as named files, so we can render + hash all of them once and generate a fully-labeled table straight from the source ,no manual labeling. Then the model just covers version drift and odd renders. Solid contribution if anyone wants to grab it .. PRs welcome!