Both hand attempts at this address managed exactly one filter and then lost the
counter, because every reading of the HUD costs a human round trip. ob_read.py
does it by template correlation over three fixed digit cells, and ob_hunt.py
uses that to run the whole method unattended: confirm the HUD on both sides of
the 0.9 s scan, then filter on each following transition, labelling the first as
the one it selected on and the rest as verification.
Two things measured rather than assumed:
* Normalisation is the point, not a nicety. The plate is translucent, so an
explosion turns it orange - on the 008 reference frame a cyan-stroke mask finds
6 of ~70 stroke columns and would silently read nothing. Per-cell mean/std
normalisation reads it correctly at 0.843.
* The accept rule is two-sided because the margin is narrow: over 12 cells from
four frames of known value the correct digit scores 0.843..1.000 and the best
WRONG digit reaches 0.789 (0 and 8 are similar outlines). So a floor of 0.80
AND a 0.05 margin over the runner-up, against a smallest observed correct
margin of 0.093. A bare threshold fitted between those two numbers would be
fitted to twelve samples.
Templates exist for 0 1 2 4 8 - the digits actually seen. Anything else reads as
"?" and callers must treat a "?" as unknown, never as a value. Rejects both
negatives tested: GAME OVER scores 0.05-0.18, the main menu 0.07-0.14.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PMRJjbxLqZtsb5Vb7KunPE
