Product recognition demo for supermarket shelf images
7c2a6f7061
The owner's brainstorm thought through to an implementable design, backed by measurements taken against the real demo images rather than assumptions: - Tag readability is a capture-distance problem first. At image.jpg's ~2 m the tags are ~14 px digits and no engine reads them; at add/image6.jpg's ~1 m (22 px digits) macOS Vision reads all five tag fields including the large red sale price, and RapidOCR reads the printed fields at 0.97-1.00 confidence but misses the stylised one. Tesseract jpn is unusable on tags. - Engine choice is an interface with backends: macOS Vision (182 ms/rail band) as the Mac fast path, RapidOCR (0.75-1.3 s/band, cross-platform) as the default elsewhere, fake for tests, PaddleOCR held in reserve. Cloud OCR is rejected: offline demo, and no owner decision to ship shelf images out. - OCR must never sit in the frame loop: a TagService runs rail-band passes on a cadence with a spatial cache and multi-pass voting (tags are static), which also fixes digit misreads. - Packaging text confirms 67% of the catalog templates (measured) but only 10%/3% on the demo shelves, because the demo catalog is fictional and the photos are a real store. Absence is therefore neutral, never a contradiction, and the demo needs one shelf whose tags and catalog describe the same store. - Compliance rules fail closed on evidence quality: a row whose tags are merely unreadable yields a capture hint, never a violation. The owner's "group of items missing price tags" is the missing_tag_group rule (>= 3 consecutive facings without a tag). - Barcode/JAN is the exact-identity path, currently undecodable at demo distance; the catalog needs optional jan and regular_price columns. - The mobile lane can start now against protocol v1: tap-to-inspect works with today's schema, and the tag fields are additive when they land. Also records the five open questions for the owner (capture assets, engine default, mobile order, catalog columns, parallel start). Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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