Product recognition demo for supermarket shelf images

kiyo9w 5094b5f6ca fix(recognition): calibrate the accept lines against unrelated shelves ... Every threshold was fitted on image6, where each crop is a facing of a product the shelf holds, so the set could not price a false accept. Measured on 4580 crops from 64 unrelated Wikimedia shelf photos at the shipped configuration: LOCAL_ACCEPT 2.0 -> 4.0 false accepts 60 (1.31%) -> 3 (0.07%) TEXT_MIN_SHARED 2 -> 4 the two-bigram reads that named sku_l12 via おい/いし On image6 that is 44 shown / 42 correct at full resolution - 95.5% precision, 65.6% coverage - the highest-coverage cell of the joint sweep that passes both G2 (>= 95%) and G3 (<= 1%). At live resolution 56% end-to-end at 97%. The text lane's evidence floor gates its answer only: zeroing the row's score would reorder the ranking and make the margin gate compare a different pair than the one it was measured on. code/sweep_accept_lines.py sweeps both axes jointly through the shipped DecisionPolicy; the earlier table swept one axis while pinning the other, so its baseline rows were not the old line.		2026-09-29 09:28:13 +09:00
.github/workflows	chore: run the Python suite from the repository root	2026-09-28 17:26:58 +09:00
recognization	fix(recognition): calibrate the accept lines against unrelated shelves	2026-09-29 09:28:13 +09:00
.gitignore	Initial commit for push folder	2026-09-18 09:13:53 +09:00