Product recognition demo for supermarket shelf images
tngo
b6c7c4b667
The text lane built each row's signature from its printed name plus the OCR of its enrolled packshot, and scored a crop against that whole string. l09's packshot prints "マヨネーズ", which is l10's actual name, so l09 could explain an l10 crop's text better than l10's own name could. Every l10 facing reads "ピュア セレクト マヨネーズ" with no "コクうま" and no "65", and l09 still won or tied them. The signature is now the row's name alone, weighted by inverse document frequency over the catalog so copy every sibling prints counts for little and the words that name one product dominate. The packshot OCR is gone: every row has a name, so it added nothing but the confusion. Measured on image6, local lane at live resolution: l10 correct 2/11 -> 5/11 wrong 3 -> 1 precision 95% -> 97% l09 hub share 34% (alarm) -> 29% (clear) Full resolution end-to-end 59% at 88% precision, live 56% at 97%. |
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| recognization | ||
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